{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据可视化分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在清洗数据之前，使用matplotlib库和seaborn库绘制柱状图、饼状图和热力图来可视化特征与特征之间、特征与标签之间的关系并进行分析。\n",
    "### 查看数据集基本信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series, DataFrame\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#读取训练集和测试集\n",
    "train = pd.read_csv('data\\cs-training.csv')\n",
    "test = pd.read_csv('data\\cs-test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150000 entries, 0 to 149999\n",
      "Data columns (total 12 columns):\n",
      "Unnamed: 0                              150000 non-null int64\n",
      "SeriousDlqin2yrs                        150000 non-null int64\n",
      "RevolvingUtilizationOfUnsecuredLines    150000 non-null float64\n",
      "age                                     150000 non-null int64\n",
      "NumberOfTime30-59DaysPastDueNotWorse    150000 non-null int64\n",
      "DebtRatio                               150000 non-null float64\n",
      "MonthlyIncome                           120269 non-null float64\n",
      "NumberOfOpenCreditLinesAndLoans         150000 non-null int64\n",
      "NumberOfTimes90DaysLate                 150000 non-null int64\n",
      "NumberRealEstateLoansOrLines            150000 non-null int64\n",
      "NumberOfTime60-89DaysPastDueNotWorse    150000 non-null int64\n",
      "NumberOfDependents                      146076 non-null float64\n",
      "dtypes: float64(4), int64(8)\n",
      "memory usage: 13.7 MB\n"
     ]
    }
   ],
   "source": [
    "#简单地查看数据集的信息。\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 101503 entries, 0 to 101502\n",
      "Data columns (total 12 columns):\n",
      "Unnamed: 0                              101503 non-null int64\n",
      "SeriousDlqin2yrs                        0 non-null float64\n",
      "RevolvingUtilizationOfUnsecuredLines    101503 non-null float64\n",
      "age                                     101503 non-null int64\n",
      "NumberOfTime30-59DaysPastDueNotWorse    101503 non-null int64\n",
      "DebtRatio                               101503 non-null float64\n",
      "MonthlyIncome                           81400 non-null float64\n",
      "NumberOfOpenCreditLinesAndLoans         101503 non-null int64\n",
      "NumberOfTimes90DaysLate                 101503 non-null int64\n",
      "NumberRealEstateLoansOrLines            101503 non-null int64\n",
      "NumberOfTime60-89DaysPastDueNotWorse    101503 non-null int64\n",
      "NumberOfDependents                      98877 non-null float64\n",
      "dtypes: float64(5), int64(7)\n",
      "memory usage: 9.3 MB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>1.202690e+05</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>146076.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>75000.500000</td>\n",
       "      <td>0.066840</td>\n",
       "      <td>6.048438</td>\n",
       "      <td>52.295207</td>\n",
       "      <td>0.421033</td>\n",
       "      <td>353.005076</td>\n",
       "      <td>6.670221e+03</td>\n",
       "      <td>8.452760</td>\n",
       "      <td>0.265973</td>\n",
       "      <td>1.018240</td>\n",
       "      <td>0.240387</td>\n",
       "      <td>0.757222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>43301.414527</td>\n",
       "      <td>0.249746</td>\n",
       "      <td>249.755371</td>\n",
       "      <td>14.771866</td>\n",
       "      <td>4.192781</td>\n",
       "      <td>2037.818523</td>\n",
       "      <td>1.438467e+04</td>\n",
       "      <td>5.145951</td>\n",
       "      <td>4.169304</td>\n",
       "      <td>1.129771</td>\n",
       "      <td>4.155179</td>\n",
       "      <td>1.115086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>37500.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.029867</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.175074</td>\n",
       "      <td>3.400000e+03</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>75000.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.154181</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.366508</td>\n",
       "      <td>5.400000e+03</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>112500.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.559046</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.868254</td>\n",
       "      <td>8.249000e+03</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50708.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>329664.000000</td>\n",
       "      <td>3.008750e+06</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Unnamed: 0  SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  \\\n",
       "count  150000.000000     150000.000000                         150000.000000   \n",
       "mean    75000.500000          0.066840                              6.048438   \n",
       "std     43301.414527          0.249746                            249.755371   \n",
       "min         1.000000          0.000000                              0.000000   \n",
       "25%     37500.750000          0.000000                              0.029867   \n",
       "50%     75000.500000          0.000000                              0.154181   \n",
       "75%    112500.250000          0.000000                              0.559046   \n",
       "max    150000.000000          1.000000                          50708.000000   \n",
       "\n",
       "                 age  NumberOfTime30-59DaysPastDueNotWorse      DebtRatio  \\\n",
       "count  150000.000000                         150000.000000  150000.000000   \n",
       "mean       52.295207                              0.421033     353.005076   \n",
       "std        14.771866                              4.192781    2037.818523   \n",
       "min         0.000000                              0.000000       0.000000   \n",
       "25%        41.000000                              0.000000       0.175074   \n",
       "50%        52.000000                              0.000000       0.366508   \n",
       "75%        63.000000                              0.000000       0.868254   \n",
       "max       109.000000                             98.000000  329664.000000   \n",
       "\n",
       "       MonthlyIncome  NumberOfOpenCreditLinesAndLoans  \\\n",
       "count   1.202690e+05                    150000.000000   \n",
       "mean    6.670221e+03                         8.452760   \n",
       "std     1.438467e+04                         5.145951   \n",
       "min     0.000000e+00                         0.000000   \n",
       "25%     3.400000e+03                         5.000000   \n",
       "50%     5.400000e+03                         8.000000   \n",
       "75%     8.249000e+03                        11.000000   \n",
       "max     3.008750e+06                        58.000000   \n",
       "\n",
       "       NumberOfTimes90DaysLate  NumberRealEstateLoansOrLines  \\\n",
       "count            150000.000000                 150000.000000   \n",
       "mean                  0.265973                      1.018240   \n",
       "std                   4.169304                      1.129771   \n",
       "min                   0.000000                      0.000000   \n",
       "25%                   0.000000                      0.000000   \n",
       "50%                   0.000000                      1.000000   \n",
       "75%                   0.000000                      2.000000   \n",
       "max                  98.000000                     54.000000   \n",
       "\n",
       "       NumberOfTime60-89DaysPastDueNotWorse  NumberOfDependents  \n",
       "count                         150000.000000       146076.000000  \n",
       "mean                               0.240387            0.757222  \n",
       "std                                4.155179            1.115086  \n",
       "min                                0.000000            0.000000  \n",
       "25%                                0.000000            0.000000  \n",
       "50%                                0.000000            0.000000  \n",
       "75%                                0.000000            1.000000  \n",
       "max                               98.000000           20.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>101503.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101503.000000</td>\n",
       "      <td>101503.000000</td>\n",
       "      <td>101503.000000</td>\n",
       "      <td>101503.000000</td>\n",
       "      <td>8.140000e+04</td>\n",
       "      <td>101503.000000</td>\n",
       "      <td>101503.000000</td>\n",
       "      <td>101503.000000</td>\n",
       "      <td>101503.000000</td>\n",
       "      <td>98877.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>50752.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.310000</td>\n",
       "      <td>52.405436</td>\n",
       "      <td>0.453770</td>\n",
       "      <td>344.475020</td>\n",
       "      <td>6.855036e+03</td>\n",
       "      <td>8.453514</td>\n",
       "      <td>0.296691</td>\n",
       "      <td>1.013074</td>\n",
       "      <td>0.270317</td>\n",
       "      <td>0.769046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>29301.536524</td>\n",
       "      <td>NaN</td>\n",
       "      <td>196.156039</td>\n",
       "      <td>14.779756</td>\n",
       "      <td>4.538487</td>\n",
       "      <td>1632.595231</td>\n",
       "      <td>3.650860e+04</td>\n",
       "      <td>5.144100</td>\n",
       "      <td>4.515859</td>\n",
       "      <td>1.110253</td>\n",
       "      <td>4.503578</td>\n",
       "      <td>1.136778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>25376.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.030131</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.173423</td>\n",
       "      <td>3.408000e+03</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>50752.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.152586</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.364260</td>\n",
       "      <td>5.400000e+03</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>76127.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.564225</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.851619</td>\n",
       "      <td>8.200000e+03</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>101503.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21821.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>268326.000000</td>\n",
       "      <td>7.727000e+06</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>43.000000</td>\n",
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      "text/plain": [
       "          Unnamed: 0  SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  \\\n",
       "count  101503.000000               0.0                         101503.000000   \n",
       "mean    50752.000000               NaN                              5.310000   \n",
       "std     29301.536524               NaN                            196.156039   \n",
       "min         1.000000               NaN                              0.000000   \n",
       "25%     25376.500000               NaN                              0.030131   \n",
       "50%     50752.000000               NaN                              0.152586   \n",
       "75%     76127.500000               NaN                              0.564225   \n",
       "max    101503.000000               NaN                          21821.000000   \n",
       "\n",
       "                 age  NumberOfTime30-59DaysPastDueNotWorse      DebtRatio  \\\n",
       "count  101503.000000                         101503.000000  101503.000000   \n",
       "mean       52.405436                              0.453770     344.475020   \n",
       "std        14.779756                              4.538487    1632.595231   \n",
       "min        21.000000                              0.000000       0.000000   \n",
       "25%        41.000000                              0.000000       0.173423   \n",
       "50%        52.000000                              0.000000       0.364260   \n",
       "75%        63.000000                              0.000000       0.851619   \n",
       "max       104.000000                             98.000000  268326.000000   \n",
       "\n",
       "       MonthlyIncome  NumberOfOpenCreditLinesAndLoans  \\\n",
       "count   8.140000e+04                    101503.000000   \n",
       "mean    6.855036e+03                         8.453514   \n",
       "std     3.650860e+04                         5.144100   \n",
       "min     0.000000e+00                         0.000000   \n",
       "25%     3.408000e+03                         5.000000   \n",
       "50%     5.400000e+03                         8.000000   \n",
       "75%     8.200000e+03                        11.000000   \n",
       "max     7.727000e+06                        85.000000   \n",
       "\n",
       "       NumberOfTimes90DaysLate  NumberRealEstateLoansOrLines  \\\n",
       "count            101503.000000                 101503.000000   \n",
       "mean                  0.296691                      1.013074   \n",
       "std                   4.515859                      1.110253   \n",
       "min                   0.000000                      0.000000   \n",
       "25%                   0.000000                      0.000000   \n",
       "50%                   0.000000                      1.000000   \n",
       "75%                   0.000000                      2.000000   \n",
       "max                  98.000000                     37.000000   \n",
       "\n",
       "       NumberOfTime60-89DaysPastDueNotWorse  NumberOfDependents  \n",
       "count                         101503.000000        98877.000000  \n",
       "mean                               0.270317            0.769046  \n",
       "std                                4.503578            1.136778  \n",
       "min                                0.000000            0.000000  \n",
       "25%                                0.000000            0.000000  \n",
       "50%                                0.000000            0.000000  \n",
       "75%                                0.000000            1.000000  \n",
       "max                               98.000000           43.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征与标签的关联性可视化分析\n",
    "构造一个绘制柱状图的函数，指定特征、特征取值范围和分箱数，绘制柱状图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bin_plot(data, num_bin, string, a, b):\n",
    "    kwargs = dict(histtype='stepfilled', color = 'salmon', alpha=0.3, density=True, bins=num_bin)\n",
    "    kwargs2 = dict(histtype='stepfilled', color = 'lightskyblue', alpha=0.5, density=True, bins=num_bin)\n",
    "    plt.hist(data[data['SeriousDlqin2yrs'] == 0][string], label=\"no\", **kwargs2)\n",
    "    plt.hist(data[data['SeriousDlqin2yrs'] == 1][string], label=\"yes\", **kwargs)\n",
    "    plt.xlim(a, b)\n",
    "    plt.legend()\n",
    "    plt.xlabel(string)\n",
    "    plt.ylabel('ratio')\n",
    "    plt.title(string + ' - yes/no')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "展现年龄特征与标签的关系。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bin_plot(train, 40, 'age', 18, 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "展现月收入特征与标签的关系。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anac\\lib\\site-packages\\numpy\\lib\\histograms.py:824: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  keep = (tmp_a >= first_edge)\n",
      "D:\\anac\\lib\\site-packages\\numpy\\lib\\histograms.py:825: RuntimeWarning: invalid value encountered in less_equal\n",
      "  keep &= (tmp_a <= last_edge)\n",
      "D:\\anac\\lib\\site-packages\\numpy\\lib\\histograms.py:824: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  keep = (tmp_a >= first_edge)\n",
      "D:\\anac\\lib\\site-packages\\numpy\\lib\\histograms.py:825: RuntimeWarning: invalid value encountered in less_equal\n",
      "  keep &= (tmp_a <= last_edge)\n",
      "D:\\anac\\lib\\site-packages\\IPython\\core\\pylabtools.py:128: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bin_plot(train, 1000, 'MonthlyIncome', 0, 30000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "绘制饼状图时，将除借款人外的家庭成员数目分为0，1，2，3-5，6-9和10-44共六类，根据这六类借款人在有风险的借款人和无风险的借款人中的占比绘制饼状图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_d = train\n",
    "train_d['NumberOfDependents'].fillna(train_d['NumberOfDependents'].median(), inplace = True)\n",
    "bins = [-1, 0, 1, 2, 5, 9, 44]\n",
    "group_names = ['0','1', '2', '3-5', '6-9', '10-44']\n",
    "dependent = pd.cut(train_d['NumberOfDependents'], bins, labels = group_names)\n",
    "train_d['NumberOfDependents'] = dependent\n",
    "def plot_dependents(data):\n",
    "    dep = data[data['SeriousDlqin2yrs'] == 1].groupby('NumberOfDependents').count()['SeriousDlqin2yrs'].index\n",
    "    dep_count_yes = data[data['SeriousDlqin2yrs'] == 1].groupby('NumberOfDependents').count()['SeriousDlqin2yrs']\n",
    "    dep_count_no = data[data['SeriousDlqin2yrs'] == 0].groupby('NumberOfDependents').count()['SeriousDlqin2yrs']\n",
    "    plt.figure(figsize=(8, 4))\n",
    "    plt.subplot(121)\n",
    "    plt.title('yes')\n",
    "    plt.pie(dep_count_yes.values, labels=dep)\n",
    "    plt.subplot(122)\n",
    "    plt.title('no')\n",
    "    plt.pie(dep_count_no.values, labels=dep)\n",
    "    plt.show()\n",
    "plot_dependents(train_d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征与特征的关联性可视化分析\n",
    "采用热力图形式呈现。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RTFzxdNfExPidKp7hZ0pMXIwSt/uV2ZGYVubImBYpVkS16tXUurW/6OLLLsyGlmeP7LgGfrt0pc46u2Ta55NrGtTW2tU/qtGN16U79rVNGqjLk93VJsSyhYrHFUu3FHtn/C4VjTn5Jf/Jycnq99xbqnP91apZL/T2uwn252l+DuN0IVMIIcnMLpCUIilevuDQE17WTjXn3PnOuc+8ohkDLscKwBx0zlWTVFrSGTq6p1BvSV97ew01lRT9H5qb7Skyt9zRLG1Tutr1a2nOjM/lnNPaNT+pQMECmT7Am5kurX6J5n0xX5I0e/rnurreVZKkWnWvOm79L2d/rWszpLrWrldLK5etkuRbSnb48OGQuBtUpQsrauvmbfpz63YdPnxYX82Zp6vq1kxXplbdmvp8xpdyzulHv/E6Vt0Lyp+vKV9O0nszx+u9meMVGxejYRMH5vqARiAVq1TQ1s1b08Zg3mfzdVXdK9OVueqaK/X5zK/knNNP3//sjV8xOef0Zu8BOu/8c9Xy7lvSyp9fvow+/OJdTZgxRhNmjFFsXIwGTwyNgFp2nMfFY4oprmSsNm3YLEn6dtlKlfY2mvZftrfgq4U6v2yZbOg1cqIWd96sMR8M15gPhqtO/as1e/pn3lz6UQULFsj0i5GZ6dIrqmnu5/MkSbOnfaY69WtJknb8uUPdn+mh7n266rwy56art3vn7rQy879coOuaNMj6zgVRhSrltW3zn9q+dYd3TfxGNa9J/4t1zbo19OWsr71r4joVKFhAxWKK6dDBQ/rrwF+SfPvdfLd0pcqULR2MbmSZ7LgGxpWM1Y/f/6xDBw+l7YVV+nzfNXDLxq1px140b3Gm+RoKylUuqz83b9eObfE6fDhZC79YrCvqXH5SdZ1zGtxnuM4pXUpN/3fjiSvkQsH+PM3PYZwuZAoh5JhZrKShkgY655yZzZHU1sy+cs4dNrMKko78JK/hLSXbKOkOedk5kg6bWZRz7rD/sZ1ze83sSUmfmNkQ+TKFjhyrtV/RJEmFjtHERZLulC9LqJWkoO6UWbN2DS1dsEytmrVW3ui86tyjY9prndt107MvPKOYuOJ65KkH1atLX40aPE7lK5bVDTc3PmH9QwcP6dul36lD9/bp3vOGm6/Xqz3eUOuWDykqKkpdez0bEhs0RuaJ1BOdH1Pnx7spJTVVTZo10vlly2ja5JmSpGYtb9SVtWto6YLlurv5/b5b0vd45rh1w0lknki169RWz7V7Xqkpqbq+eUOVKVtaMyb7/gp3U8sbVKP2FVq2cIVaN38w7Zb0krR21Y/6YuZXOr9cmbSsoPsfv081al8RtP5kp6w8j5/s/Lheeu4VJScn66xSJdWlp++1oQNGav2632RmKnlWCXXo/lT2djoHGNe9hepUK62Ywvm1/oP26j12rsbNWhXsZgXVVXWu1JIFS3XnTfcoOjpaXXs9m/bas493VecXOygmLkZt2/uWDo8cNEblK5XTjbf4blU9ZtgE7d2zT2/29d21LDIyUiPf891Rq3uHHtq7d5/y5Mmjp597Mm1D6lAVmSdSbZ99WN2f6KGUlFQ1anatSpc9TzMn++7UdmPLJrri6su1fOEK3X/zo4qOzqunX3xCkrR75x71fvZlSb4l2/Wuv0bVa10WtL5ktay6Bla5uLLqXldHD931mCIjI1W+UjnddKsvW23426O0aeNmRUREqMRZcXqmW+hdAyPzROrBDq31UvtXlJqaqgY31dO5F5yjOVN8S+iub3Gddu/co85tuuvggYOyCNPMSbPV/73XtHH9Js2fvUDnlT1XHe/tKkm669HbdVmtS4PZpSwTjM/T/Bz+9yIs929XkRUsFPbxAMwsRdL38i3fSpYv4PKmcy7VuzX9S/Jl8pikBEk3SzqyD1CCfHsKzZf0mFfnVUnNJH3nnGtlZvudcwX93m+6pA8krZc0zjvGV5Lucc6VMbNikuZ47XlZUj5J1Z1z7cysjKTRkmK8em2cc+nvMesnK5aPhZNUlxrsJuRqyS4l2E3I9c6IOJlVpTiWC24cF+wm5GobP70/2E3I9fYf3h/sJuRq+SLzBbsJuV7iocwbsePkxUTHnLgQjuus/KVz/19vJbVbsCzH/141sHaNbB9rMoUQEpxzx9xt0jmXKt8t5jPeZn6u9y9Qnc6SOvs9Lpjh9aZ+Dyv4ff289/ou+Ta19jfWe22DpNDOeQcAAAAA5HjsKQQAAAAAABCGyBQCAAAAAAAhLQS2MM0SZAoBAAAAAACEIYJCAAAAAAAAYYjlYwAAAAAAIKRxS/rAyBQCAAAAAAAIQwSFAAAAAAAAwhDLxwAAAAAAQEjj5mOBkSkEAAAAAAAQhggKAQAAAAAAhCGWjwEAAAAAgJBmrB8LiEwhAAAAAACAMERQCAAAAAAAIAyxfAwAAAAAAIS0CHPBbkKORKYQAAAAAABAGCIoBAAAAAAAEIZYPgYAAAAAAEIadx8LjEwhAAAAAACAMERQCAAAAAAAIAwRFAIAAAAAAAhD7CkEAAAAAABCWoS4JX0gZAoBAAAAAACEIYJCAAAAAAAAYYjlYwAAAAAAIKRxS/rAyBQCAAAAAAAIQwSFAAAAAAAAwhDLxwAAAAAAQEgz4+5jgZApBAAAAAAAEIYICgEAAAAAAIQhlo8BAAAAAICQFsHdxwIiUwgAAAAAACAMERQCAAAAAAAIQywfAwAAAAAAIY27jwVGphAAAAAAAEAYIigEAAAAAAAQhlg+BuRwEUbs9lSkuNRgNyFXS2X8EGQbP70/2E3I1Uo3GR3sJuR6K6feHOwm5Gr58uQPdhNyPSeWvJwS45ZTwPEQFAIAAAAAACGNP7UHxrgAAAAAAACEIYJCAAAAAAAAYYjlYwAAAAAA/D979x0eRdXFcfx7CJEAoScBBRTpoCJIEWkCAoJSFLvYG3ZREFBQiqKCqCgiRRDQFykiKiCCBem92gsqHUlCDUUlyX3/2CFskqUokE12f5/n4WF35s7svXdnZjdnz70jIU23pA9MmUIiIiIiIiIiImFIQSERERERERERkTCk4WMiIiIiIiIiEtKUEROY+kVEREREREREJAwpKCQiIiIiIiIiEoY0fExEREREREREQpruPhaYMoVERERERERERMKQgkIiIiIiIiIiImFIw8dEREREREREJKRZsCuQTSlTSEREREREREQkDCkoJCIiIiIiIiIShhQUEhEREREREREJQ5pTSERERERERERCWi7dkj4gZQqJiIiIiIiIiIQhBYVERERERERERMKQho+JiIiIiIiISEjTLekDU6aQiIiIiIiIiEgYUlBIRERERERERCQMafiYiIiIiIiIiIQ03X0sMGUKiYiIiIiIiIiEIQWFRERERERERETCkIaPiYiIiIiIiEhIM91+LCBlComIiIiIiIiIhCEFhUREREREREREsjkza2lmP5vZOjPrfoxytc0sxcyuPd4+NXxMREREREREREJaTh89ZmYRwBCgObAZWG5mU51zPwQo1x+YdSL7VaaQiIiIiIiIiEj2VgdY55z73Tn3DzABaBeg3CPAh0D8iexUQSERERERERERkSAzs/vMbIXfv/v8VpcENvk93+wt89++JHA1MOxEX1PDx0REREREREQkpOUyF+wqHJdzbgQw4iirA42Ay9ioQUA351yKneDt1hQUEhERERERERHJ3jYDpf2elwK2ZihTC5jgBYRigCvMLNk59/HRdqqgkIiIiIiIiIhI9rYcqGBm5wJbgBuBm/0LOOfOPfzYzMYA048VEAIFhUREREREREREsjXnXLKZPYzvrmIRwDvOue/N7H5v/QnPI+RPQSERERERERERCWk5/Zb0AM65GcCMDMsCBoOcc3ecyD4VFBKRo3LO8caAISxZsIw8UXl4qm9XKlWpkKnc1i3b6NOtH3v3JFGxSnl69utOZGQkn3/6Fe+PmQBA3rx56dzjMcpXKpfVzTjtli1cwZCBQ0lNSeWKq1ty0503pFvvnGPIy0NZumA5eaLy0LVPZyp6/Xi0bZ/r9gKbNmwGYF/SPqILRDNiwlvs2b2XPl2f5+fvf+HyNs15tPtDWdvY02zFopUMHfg2qamptLyqOTfccV269c45hg4cwfKFK8kTlYfOvR+jQuXy/PP3P3S5tzuHDh0iJSWFhpfV59aOHQD47effGfziW/zzzz9ERETwcLcHqHR+xWA0L8s55xg84C2WLFxOVFQeuvfpknbs+du2ZRt9u7/gncMVePr5rkRGRrLhj4307/UKv/60jrsfvoMbb7suwKuEFuccr/cfwpIFS8kTlYenn+tKpSqZj5etm7fRu9vzJO1NomLlCvR84fB170vGjfZd9/Lly0vnHp3SrnsfjPuQaR/OwDlHm2uu5PpbrsnStmU3w7q2oVXdiiTs3k+tu/7Tj5shadXi1bz96mhSU1Np3vYyrr396nTrnXO8/epoVi5aRZ6oPDz2zEOUq1wWgGkTPuXzT77COUeLds1oe9OVACz8ajHj357E5vVbeHn0i1SoEnqfxYelXfcWLPNd9/o+efTrXrcX2Ltnr++616+b33VvIL/+uI67H76TG2/3Xff+/vsfHrvrCd/nTHIKlzZryJ0P3p7VzctyqxevZfSg90hNSeWyto25+ra26dZvWb+VIf2G88fP67mp4/W07eA75hK37+DNvkPZvWMPlsto1q4pV97QMhhNyHKn6xgE6N9rIIvnLaVw0cKM+fDtrGyWhDDdkl5OGzNzZvae3/PcZpZgZtP/4/4Km9mDfs8bH21fZjbHzGodZ3/7/ks9wsmSBcvYvHEL708dy5PPPM6r/V4PWG74oLe5/pZrGD9tLAUKFuDTjz4D4MySJRg86lXGfPA2t993Cy8/91pWVj9LpKSk8Eb/Ibw4+Hne+XAEs2fOYf3vG9KVWbZwOZs3buXdT97hiZ6P8fqLbx5322f6P82ICW8xYsJbNLysAQ2a1gfgjDxncOcDt3H/4/dmbUOzQEpKCkP6D+P5N3oz4oMhzJk1jw2/b0xXZvnClWzdtJV3PhrOYz0e4s0XhwIQeUYk/Yf1Y+j4wbz1/husWLSKH7/9CYBRb4ymw7038tb7b3Brxw6MfGN0VjctaJYuWM7mjVsY98loOvfsxGsvvBGw3PDXR3Fth/aMmzqG6ALRzPhoJgAFCxXg0W4PcsNt12ZltYPKd93bzPhp79L12Sd45fnA171hrx++7r1LgYLRTE+77p3Jm++8xtjJI7n9vlsY0PdVAH7/9Q+mfTiDEeOGMPqDt1k0b0la4DdcvTdzLe26jQt2NbKVlJQUhr88il6DevDmhNeY//lCNv6+KV2ZlYtWs23TNoZNHsxD3TsydIDvD8MNv23k80++YuDoF3n9fwN918uN2wA4u2xpuvfvwnk1qmR5m7LaUu+7y7ipY+j8TCde63eU696gkVx7S3vGTRtLdMEM172uD2W67p1xRiSvvv0yoyYNZ+TEYSxbtILvv/nhtLcnmFJSUhn1yhh6vNqV18YPYOEXi9n0R/rrVnTB/Nz1+G20ufnKdMsjInJx26MdGDThZV54uw+zPvwi07ah6nQdgwAt27ZgwFsvnNb6S/hRUEhOp/3A+WaW13veHN+EWP9VYeDB4xWSU2fBnEVc3ro5ZsZ51aqyL2kfiQk70pVxzrFq+RoubdYIgJZtWjD/64UAXFD9PAoULADAedWqkLA9IWsbkAV++u5nSpY6k7NKnUlkZCRNLr+URXMWpyuzcM5iWrS+DDOjarUq7Evax46EHSe0rXOOuV/Mo2nLxgDkzRvFBTXOJ/KMyKxqYpb5+ftfObP0mZxZqgSRkZFc2qIRi+cuTVdm8dwlXHZFU8yMKhdUZl/SfnYk7sTMyJvPd6lJTk4mOTmZtNtwmnFg/0EA9u/bT7HYolnarmBaONf/HK7i66/jnsPNWTBnEQBFihah8nmViMgdkeV1D5YFXy+kZZsWx7/uLVtN4+aXAr4v6fNnB7ruVU277m34YyNVq1UhKm8UuXNHUL1mNebNXpCFLct+Fn6zkZ17Dwa7GtnKrz+so0SpEpQoWZzIyEgaNq/Psnkr0pVZNm85TVpdiplR6YKK7E/az87EXWxev4WK51cgT1QeInJHcH6NqiyZuwyA0ueWotQ5JYPRpCy3cM5iLm/dLN05fPzrXgsWeN9dihQtQuXzKxGRO/2ACjMj39E+Z0LUuh9+o0Sp4hQvGUdkZG7qN6vLinkr05UpVLQQ5auWI3eGz4kiMUUoW8k3323e/HkpWeYsdibsyrK6B9PpOgYBLqxZLe0zRv69XOay/b+g9EtQXlXCyWfA4Z8ObgLGH15hZkXN7GMz+8bMlphZNW95bzN7x8v2+d3MHvU2eQkoZ2ZrzOxlb1m0mU02s5/MbJxl+HQ2s7vN7DW/5/ea2asZyjT2XivTfsystpktMrO1ZrbMzAqYWZSZjTazb81stZk18cre4bVnmpn9YWYPm9kTXpklZlbUK1fOzGaa2Uozm29mlU9VZ59qifGJxJWITXseWzyWxPjEdGX27N5LdIHotC8DscVjSIxP/8EHMP2jz7i4QZ3TW+EgSEzYQax/H8Vlbn9i/A5ii/uXiSUxYccJbfvtqu8oUrQIpc4O/S/zO+J3EFs8Ju15TFwxdmTojx0JO4gtcaRMbPEjZVJSUnjw5ke5sfmtXHRxDSqfXwmA+zvfy8jX3+GWK+9k5OvvcOfDoZ/uf1hCfIZjrHgMCRn6NNA5nJDhPA8nCfGJxBX/t9e9zGUg/XXv3PJlWLvyG/bs3sNfB/9iyYKlxP8ZeoFyOTk74ncSU7xY2vNicUUz/TG5IyF9mZi4YuxI2MnZZUvzw+of2bsnib//+puVi1aRuD38zuWE+ERiS8SlPQ90TQt83cv83SWjlJQU7r6+I1c1vY5adS+i6gWhnXm1M2EnxeKOHGtF44qy4z8EduK3JfDHLxuocF7oDlv0dzqPQZHTQUEhOd0mADeaWRRQDfD/2b8PsNo5Vw14GnjXb11l4HKgDtDLzCKB7sBvzrnqzrknvXI1gE5AVaAsUD/A67f1tge4Ewg0diTTfszsDGAi8Jhz7kKgGXAQeAjAOXcBvkDXWK99AOfjuy1gHaAfcMA5VwNYDNzmlRkBPOKcqwl0Ad7KWBkzu8/MVpjZivdGBS+13rnM0epMv4oFKJNxFrdVy9fw6cczuf+xe05h7bKJE+kjApTBTmjb2bPm0MTLEgp1LlA/ZeiPgIebVyYiIoK33n+D/80Yzc/f/8L6db6heNMnz6DjE/fwv09H0/GJe3jtucBp3CEp4DF2ImVC+9fvYwn0G13m4/D4fbZq2Wo+/egzHujkG+pZpuw5dLjzRh7v2JUuD3anfMVyYZWBJf/dCR1/+LKB2t/Wjl6PPEfvx/pRpkIZckWE4TH2H7+7nMhlLyIiglGThvPBrPH8+N3P/L7uj/9ay5zhGJ+5J+rggb8Y+NQg7ux0K/ny5ztFFcvmTuMxKHI6aKJpOa2cc9+YWRl8wZMZGVY3AK7xys02s2JmVshb96lz7m/gbzOLB4of5SWWOec2A5jZGqAMkJaP75zbb2azgdZm9iMQ6Zz79gT3swfY5pxb7u1rr7e+ATDYW/aTmW0ADs9C+rVzLglIMrM9wDRv+bdANTOLBuoBH/h9OOQJ0G8j8AWP2H5wU5bmEU6Z8AnTp/jeqsrnVUz3S3bC9gSKxRZLV75QkULsS9pHcnIKuXNHkLA9kRi/Mr/98jsD+rzCy0NepFDhQoSamLgYEvz7KD4x0/CkmLiYdEPnEuITKBZblEOHDh1z25TkFObPXsiwcYNPYwuyD18/HfklLTF+B0Uz9WUxEv48UiZhe+Yy0QWiqVbzAlYsXkmZ8ufw5fTZPNDlPgAaNmvAoOdDuz8/mjjV7xyulP4Yy3B+wvHP4XAwZcLHTPPrs/jtx77uFc7UZ+nLrPvlN/oHuO61bn8FrdtfAcDwN0amy0gSAV9mUOL2I9kCO+J3UjQm83XQv4z/tbJ528to3vYyAN576/10WR6h7KN0310qkfBnfNq603HdK1Awmuq1LmTZwhWULX/uqWlENlQ0rmi6jN2d8TspGlP4hLdPTk7mlacH0fDy+lzcuPZpqGH2kdXHoPw3irsFpkwhyQpTgYH4DR3zBDovDwdA/vZblsLRA5gnUm4kcAdHzxI62n6Mo/xofJR9ZNxPqt/zVG+fuYDdXrbT4X/ZKve4/Y3teGfScN6ZNJyGTeoza/oXOOf4/psfyB+dP9MHlplRo1Z15n45D4CZ0z6nQeN6AGzftp2enXvT4/nulD6nVJa3JStUPq8SWzZtZduWPzl06BBfz5pLvUvrpitT79K6fD7ddzeYH775kfzR+SkWW+y4265cupqzy5RON/QslFWqWoGtm7byp9cfcz+fR91G6Ycc1r30Yr6aMRvnHD9++xP5o/NRLKYou3ftYV+Sb+74v//6m9XL1lC6jO+YKxZblG9WfgfAmuXfcFbps7K2YVns6hvaMmriMEZNHEaDJvX8zuEjx54/3zl8od85/AX1G18SjKoHTfsbr2L0pBGMnjSChk3qM3Pa52nXveijXfdqV2fOF3MBmDn1cxo28bvuPdGbnv2e4uwypdNtt2vHrrQy875aQLNWTU9/4yRHqVClPNs2bWP71u0cOnSI+V8spE6j9PfNqNOwFl9/NhfnHD9/+wv5o/NRNKYIALt37gEg4c8EFs9ZSqMWGROoQ9PVN7Zj1KThjJo0nAZN6jNr+pfpvrsc/7r3OfW97y5Hs3vnbpL2HvmcWbl0FWefW/qY2+R05auUZdumP9m+NZ5Dh5JZ+OUSajWseULbOucY2u9tSp5TkjY3XXGaaxp8WXEMipwuyhSSrPAOsMc5962ZNfZbPg/oADznLU90zu09RlpqEvCvZ1Zzzi01s9LARfiGsJ2on4CzzKy2c265mRXAN3zscL1nm1lF4GzgZ2//x6vLXm++oeuccx94cxdVc86t/bftygp1G17M4gXLuKnNbb5b0vd5Mm3dkw89TbdeTxATF8P9ne6hd7d+jBwymgqVynPl1a0AGDPif+zZvTftjkcRuSN4+/1Mo+VytIjcETzS7UG6PdSD1NRUWrVtQZlyZZg2+VMA2lx7JRc3qMPSBcu5td1dREXl4cneTxxz28O+/nxO2gTT/m6+8jYO7D/g+4I2ZzH93+pHmbLnZEVzT6uI3BE8+OT99HikF6kpqbRo24wy5c7h08m+uzpdeW0r6tSvxfKFK7jrqvvIE5WHJ3o9BsDOxJ280msQKampuNRUGjVvwMUNfQGlx3o+zLCBb5OSksIZZ5zBYz0eDlobs1rdBnVYumAZHdreQZ6oPHTr3SVtXbeHe/Dks08QE1eMjo/dQ9/uLzDqrbFUqFSOK67y3TZ4R+JOOnZ4mAP7D2BmTB73EWM/fJv80fmD1aTT7pKGF7NkwVJubH0rUVFRPNXX/7r3FN16dSYmLoYHOt1L767P+657lY9c90YPf489u/fy6gu+u5ZFREQwcrzvLnk9O/dmz5695M6dm8effjTsJwsd27M9DaufQ0yhfKyb1Innxsxh7Iw1wa5WUEXkjuC+LnfT+9F+pKamclmbJpxdtjSfTfkcgFbtW1Cz/kWsWLSa+695hDxRZ/DIMw+lbd+/+0D27kkid+7cdHzyHqILRgOweM5S3h74Dnt27+W5x1/k3Ipl6PNGz6C08XSr27AOSxcspUOb233XvT5+172HnuZJ77tLx0730rdbP0YNGeO77l3td927+SG/694Uxk4ZyY7Enbz4zABSU1NJTXU0adGIeo3qHq0aISEidwR3d76Dfp36k5qaSpPWl1K6bCk+n/IlAC3aN2PXjt10v7MnB/cfxHLl4tOJn/Ha+AFsWLeJeTMXcHa50nS57SkAbr7/Bi6qVz2ILcoap+sYzB+dn77d+7FmhW9+umtb3MSdD9yW9vkj8l9ZoHHJIqeCme1zzkVnWNYY6OKca+1NvDwaOBc4ANznDTfrDexzzg30tvkOaO2cW29m7+ML7HwGfHp4X165N4EVzrkxZjbHW7fCW9cdqO6cuzFj/fzrFGA/tfENFcuLLyDUDEgGhgE1vcdPOOe+NrM7gFrOuYe9/az3nif6rzOzc4GhwJlAJDDBOdf3aP2Y1cPHQs2h1ORgVyFHO5R6KNhVyPGiIjKNEJV/ISJXGM6Jcgqd0+qdYFchx1v90VXBrkKOVihP4WBXIcdLOBh//EJyVLF5445fSI7pzLxnh8TIq0Hfz872f1d1Oq9plve1gkISFsxsOvCac+6rYNfl31JQ6OQoKHRyFBQ6eQoKnRwFhU6OgkInT0Ghk6Og0MlTUOjkKCh08hQUyjrBCAppTiEJaWZW2Mx+AQ7mxICQiIiIiIiIyOmiOYUkpDnndnPkzmAiIiIiIiIShpQRE5j6RUREREREREQkDCkoJCIiIiIiIiIShhQUEhEREREREREJQ5pTSERERERERERCmlm2v/lYUChTSEREREREREQkDCkoJCIiIiIiIiIShjR8TERERERERERCmjJiAlO/iIiIiIiIiIiEIQWFRERERERERETCkIaPiYiIiIiIiEhI093HAlOmkIiIiIiIiIhIGFJQSEREREREREQkDGn4mIiIiIiIiIiENGXEBKZ+EREREREREREJQwoKiYiIiIiIiIiEIQ0fExEREREREZGQpruPBaZMIRERERERERGRMKSgkIiIiIiIiIhIGNLwMREREREREREJaRbsCmRTyhQSEREREREREQlDCgqJiIiIiIiIiIQhBYVERERERERERMKQ5hQSERERERERkZCWS7ekD0iZQiIiIiIiIiIiYUhBIRERERERERGRMKThYyIiIiIiIiIS0kz3pA9ImUIiIiIiIiIiImFIQSERERERERERkTCk4WMiIiIiIiIiEtJyobuPBaJMIRERERERERGRMKSgkIiIiIiIiIhIGNLwMREREREREREJabr7WGAKColkc6ar10mJyBUR7CrkaAl/xQe7Cjlemehzgl2FHG3XP7uDXYUcbfVHVwW7Cjlejas/DnYVcrTtnz8Y7CrkeEPX/xXsKuRonc+PCnYVRLI1DR8TEREREREREQlDyhQSERERERERkZCm8ReBKVNIRERERERERCQMKSgkIiIiIiIiIhKGFBQSEREREREREQlDmlNIREREREREREJaLnPBrkK2pEwhEREREREREZEwpKCQiIiIiIiIiEgY0vAxEREREREREQlpuiV9YMoUEhEREREREREJQwoKiYiIiIiIiIiEIQ0fExEREREREZGQpruPBaZMIRERERERERGRMKSgkIiIiIiIiIhIGNLwMREREREREREJabr7WGDKFBIRERERERERCUMKComIiIiIiIiIhCENHxMRERERERGRkGa6+1hAyhQSEREREREREQlDCgqJiIiIiIiIiIQhBYVERERERERERMKQ5hQSERERERERkZCmjJjA1C8iIiIiIiIiImFIQSERERERERERkTCk4WMiIiIiIiIiEtLMLNhVyJaUKSQiIiIiIiIiEoYUFBIRERERERERCUMaPiYiIiIiIiIiIU2DxwJTppCIiIiIiIiISBhSUEhEREREREREJAxp+JiIiIiIiIiIhDTdfSwwZQqJiIiIiIiIiIQhBYVERERERERERMKQho+JiIiIiIiISEjT4LHAlCkkIiIiIiIiIhKGFBQSEREREREREQlDGj4mIkflnOP1/kNYsmApeaLy8PRzXalUpWKmcls3b6N3t+dJ2ptExcoV6PlCdyIjI5n/9UJGDhlNrly5iIiI4NEnH6TaRRcEoSVZxznH4AFvsWTBMqKi8tC975NUrFIhU7ltW7bRt9sL7N2zl4pVKvB0v25ERkay4Y+N9O81kF9/XMfdD9/Jjbdfl7ZN/14DWTxvKYWLFmbMh29nZbOCZs2Sbxg76H+kpqTStM2ltLutTbr1W9ZvZVi/t/njlw3c0PFa2tx8Rdq6h9s/Qd58UeSKyEVERC5eeKdvVlc/KJxzvNr/DRbP9523zzz3FJWrBj5ve3btw969e6lUpSK9X+hBZGQkK5evputjPTir5JkANL6sIXfffwcAV7W8gfz58pIrIoKIiAjGTBiRlU3LcisWrWLYwLdJTU2l5VXNuf6Oa9Otd84xbODbLF+4kjxReejc+zHKVy4HwO1t7iVfvrxpx98b770ajCZkuVWLV/P2q6NJTU2ledvLuPb2q9Otd87x9qujWbloFXmi8vDYMw9RrnJZAKZN+JTPP/kK5xwt2jWj7U1XArDwq8WMf3sSm9dv4eXRL1KhSrksb1d2NKxrG1rVrUjC7v3UumtYsKuTLTnneOXFQSycv5ioqCh69etB5aqVMpXbsnkrPZ7sxd49vuth35eeJTIykn1J+3ime1+2b9tOckoyt9xxM22vvjIILQmebWu+ZfW743GpjrJNGlKl3RUBy+347Q++eqYflzx2P6UvrkXKP4eY3bc/KYcO4VJSKX1xTc6/7qqsrXyQOOd45aXXWTR/CVFReXj2+aePetz17NqbvXuSqFSlIn1e7ElkZCTvjX6fmZ9+AUBKSgrrf9/ArHnTKFSoIM898yIL5i2iSNEiTPjo3axuWo5nGkAWUI7LFDIzZ2av+D3vYma9T9G+x5jZtccvecx9lDKzT8zsVzP7zcxeN7Mz/NaPN7NvzOxx8+nplf3FzL42s/NOviUB61XRzGaY2Toz+9HMJplZ8ZPY3xwzq+U9nmFmhb1/D/qVKWNm3wXYtq+ZNfuvr32MOl3tHR+V/8O2jc1suvf4DjN781TXLydasmAZmzduZvy0d+n67BO88vzrAcsNe/1trr/lGsZPe5cCBaOZ/tFnANS8+CLGfPA2oyeNoHufLvTv80rA7UPJ0gXL2LxxC+OmjqHzM514rd8bAcsNHzSSa29pz7hpY4kuGM2Mj2YCULBQAR7t+hA33Jb5UtSybQsGvPXCaa1/dpKakso7A9+l+ytdeOX9l1j45RI2/7ElXZnogtHc8fittL6pVcB9PPPmU/Qf+3zYBIQAFi9YyqYNm/lg+jieerYLA54PHIwYMmgYN916HZOnv0/BggWYOuXTtHXVL6rGex+M4r0PRqUFhNK2GzWI9z4YFfIBoZSUFIb0H85zb/Ri+AdvMmfWfDb8vjFdmeULV7J10zZGfTSMR3s8xJsvDk23/qXhzzPk/UFhExBKSUlh+Muj6DWoB29OeI35ny9k4++b0pVZuWg12zZtY9jkwTzUvSNDB/gC3Bt+28jnn3zFwNEv8vr/Bvr6duM2AM4uW5ru/btwXo0qWd6m7Oy9mWtp121csKuRrS2av5iNGzczZcZEnu7dlZeeGxiw3JuvDeXmW29gyoyJFCxYgE8+nA7AB+M/pGy5Mrw/ZSzDR7/J6y8P5tChQ1nZhKBKTU1l5ehxNOr2OC0HPseGRUvZs3lrwHLfvD+ZEheen7YsV2RuGvfsQsv+fbj8pV5sW/sdib/+lpXVD5pF85ewacNmPvx0PE/16kr/5wN//33ztWHcdOv1fPjpeAoULMAnU3zH3a133sy4yaMZN3k0Dz3WkRq1qlOoUEEArmzXiteHBj6ORf6rHBcUAv4G2ptZTLAr4s/MIszMgCnAx865CkBFIBro55UpAdRzzlVzzr0GPATUAy50zlUEXgSmmlnUKa5bFPApMNQ5V945VwUYCsRmKPefMsecc1c453YDhYEHj10anHPPOue+/C+vdRw3AQuAG0/DvsPSgq8X0rJNC8yM86pVZV/SPhITdqQr45xj1bLVNG5+KeALXMyfvRCAfPny4jst4K+Df6U9DmUL5yzm8tbN0vXZjkB9tnwNlzZrBEDLNi1Y8LWvz4oULULl8ysRkTvz6XhhzWoUKFjg9Dcim1j3w2+UKBVH8ZJx5I7MTb1mdVkxf1W6MoWKFqRc1bJE5I4IUi2zn3lfL+CKNpdjZpx/4XlHPW9XLFtNE++8vaLt5cz7ekEwqptt/fL9r5xVugRnlipBZGQkl7ZoyJK5y9KVWTJ3GZdd0QQzo8oFldiXtJ+diTuDVOPg+/WHdZQoVYISJYsTGRlJw+b1WTZvRboyy+Ytp0mrSzEzKl1Qkf1J+9mZuIvN67dQ8fwK5InKQ0TuCM6vUTWtv0ufW4pS55QMRpOytYXfbGTn3oPBrka2NvfrBVzZtiVmxgUXnk9SUhKJCYnpyjjnWL50JU1bNAbgynZXMHf2PN9KM/bvP4BzjgMHDlKwUEEiIsLn82bnut8pUCKO6OKxROTOzdmX1GHLitWZyv068ytKXVyTPH7fUcyMyCjfnzSpKSmkpqSExfdA8D6H046780hK2hfwuFuxbBVNmzcG4Mq2LZk7e36mfc2a8SWXt7os7flFtapT0AsQiZwqOTEolAyMAB7PuCJjpo+Z7fP+b2xmc73smF/M7CUz62Bmy8zsWzPzz0NuZmbzvXKtve0jzOxlM1vuZfl09Nvv12b2PvAt0BT4yzk3GsA5l+LV8y4zywd8DsSZ2Rozawh0Ax5xzh3wyn8OLAI6HK6/mb1iZqvM7Cszi/WWlzOzmWa20qtrZb/2v2Fmi8zsd7++uBlY7JybdriRzrmvnXPfeVkxH5jZNOBzM8tvZu94bV1tZu28fec1swle+ycCef36eb0XpHsJKOe17+WjvYH+75O3bR+vjd/6teVo9TjPe9/WeHWp4C2PBuoDd+MXFPLeozlmNtnMfjKzcV7wDjNr6S1bALQ/Wn399vWEmX3n/evkt/xj77343szu81u+z8z6mdlaM1tiXmaWmV3n7WOtmc073usGU0J8InHFj8QOY4vHkhif/kNtz+69RBeIJrf3R3nGMvO+WkCHdnfQ9eEedO/TJWsqHkQJ8YnElohLex5bPIaE4/ZZDAnx6f9oF9iZsItixYulPS8aW5SdCbtOeHszeKHTAJ6681m+/Pjr01HFbCkhPpE4v2MwrngsCfEJ6crs2b2HAgWiye0FH+OKx5Gw/chx+u3a77nl2rvo9MCT/L7uj7TlBjzasQu333AvH0+eenobEmSJ8TuILX7k96eYuGLsyHCe7kjYQUwJvzLFY0j0yphBj4d68cgtTzBjyqysqXSQ7YjfSYzfOVssrmimoPiOhPRlYuKKsSNhJ2eXLc0Pq39k754k/v7rb1YuWkXi9vTXTpF/K2F7AsXTXQ/jiN9+vOthLPHeNfP6m69h/e/radWkHTddfRudu3ciV66c+OfTf3Nw127yFiua9jxfsSIc3LU7XZkDO3exZfkqyjVrnGn71NRUZnXvzScdH6fEBVUpVr7saa5x9hAfn/G4iyU+03fB9Mdd8RKxmb4v/nXwL5YsXEoTL3Akcrrk1DmFhgDfmNmAf7HNhUAVYCfwOzDSOVfHzB4DHgE6eeXKAJcC5YCvzaw8cBuwxzlX28zyAAvN7HOvfB3gfOfcH2b2KLDS/0Wdc3vNbCNQHmgLTHfOVTezgkB+51zGPMoVwOEhZPmBVc65zmb2LNALeBhfUOx+59yvZnYx8Ba+gBTAmUADoDIwFZgMnJ+xXhlcAlRzzu00sxeA2c65u8ysMLDMzL4EOgIHnHPVzKwasCrAfrp7fVEdfMPHjvGa/hKdcxeZb+hZF+AeoMdR6nE/8Lpzbpz5huUd/rnmKmCmc+4XM9tpZhc55w7XsQa+Pt0KLATqm9kK4G18/bYOmHisCppZTeBO4GJ8fxctNbO5zrnVwF1e3+UFlpvZh865HfjevyXOuR7esXov8DzwLHC5c26L17ZAr3cfcB/Ay2++xG13dzjBrjy1XIBlluFXHucyl/Iv0+iyBjS6rAFrVn7DyCFjGDTiqPHC0HCc/jh6mdNVodDyb/qpz7BnKBpbhD0799KvU39KnnMmVWr869GlOc7xzklfmczbHS5SuUpFPp41kXz58rFo/hK6durB5OnvAzDi3SHExsWwc8cuHu3YmXPKnEONWhee8jZkW//i+vfKqJcoFluM3Tt38/RDvShdphQXXHRaRohnayf0mYEvG6j9be3o9chzROWNokyFMuQKo4wMOT1O7Hp49DJLFi6jYuUKDH1nMJs3beHheztRveaFREfnPz0Vzm4CfVhksPrd8VS7+dqAwbJcuXJx+Uu9+Wf/ARa++ia7N22mcOlSp6Om2UvA69yJXAvTl5k/dyHValyQNnRMTp6+bweWI4NCXqDlXeBR4ETzZpc757YBmNlv+LJ2wJfh08Sv3CTnXCrwq5n9ji+40gKo5pd5UwioAPwDLHPOHf4Z1TjK39FHWR6If9lUjgQr/gdM8TJi6gEf+H2o5fHb/mOv/j/Yic8Z9IVz7nC+ewugrZkdTumIAs4GGgFvADjnvjGzb05w3ydiivf/So5k7BytHouBHmZWCpjinPvVW38TMMh7PMF7fjgotMw5txnAzNbgC/ztA/44vL2Z/Q8vCHMUDYCPnHP7vfJTgIbAauBRMzs8k2ZpfMfGDnzHx3S/tjX3Hi8ExpjZJL+2p+OcG4Ev+Ef8X5tP9Ng5JaZM+JhpU2YAUPm8Sul+UUvYnkCx2GLpyhcuUoh9SftITk4hd+6IgGUAqtesxtZNW9m9aw+FixQ6vY3IYh9N+ITpfn2W8Gd82rqE7YnEZOiPQpn6LHMZgaKxRdix/UiWwc6EnRSJKfKvtgffELPajWqy7sffQzYoNHnCR2lzYFQ5rxLxfsdg/PYEYmLTj7guXKQQSUn7SE5OJnfu3MRvjycmzlcmv98fO/Ua1mVAv9fYvWs3hYsUJtYrU7RYES5t2pAfvvsxZINCMXHF0mVPJcbvoFhs0QxlYkj806/M9sS0Moevg4WLFqZe47r8/P0vIR8UKhZXlES/c3ZH/E6KxmTss2LpyiTG76Co12fN215G87a+YRLvvfU+xeJ0XZR/b9L4D9MyGaueX4Xt6a6H8WnXscMKFymc4XqYQKx3zZz20afcfs8tmBmlzy7FWSXPZMMfGzjvgqpZ16Agylu0CAd3HBkSe2DHLvIWKZyuzK7fN7D4jeEA/JO0j21rvsVy5aJU7YvSypyRPx+xVSrx59rvQjYo9MH4KXz8oW9QRtXzK2c47hKIjcv4/Tn9cbf9zwRiMpT5/LOvaNHqlE/DKpJJTs5/HIRvqJB/qD4Zr03eEKEz/Nb97fc41e95KumDYxn/AHf4AjWPOOeqe//O9YZ6Aez3K/s9UMt/Yy8jqDSQLiPIObcX2G9mGfMoLwJ+IDDntW+3X12qe3MEBWrn4ajR90DNo+wzYxsMuMZv32c75370e/3T4XCdUzjyXgSsh3PufXwZVweBWWbW1MyK4cv4GWlm64EngRvsSNTMv0/8X+PftCdgXNnMGgPNgEuccxfiCxIdnhPqkDvyM0Da6zrn7gd64jsu1nj1zzba33gVoyeNYPSkETRsUp+Z0z7HOcf33/xAdHT+TMELM6NG7erM+WIuADOnfk7DJvUA2LxxS9ovIT//+AuHDh2iUOHQ+7Xj6hvbMWrScEZNGk6DJvWZNf3LtD7LH50/U5DMzKhR60LmfukbPThz2ufUb1wvGFXP1spVKcufm7cTvzWB5EPJLPpyCTUb1Dihbf86+DcH9x9Me/zNsu8oXTY0v4gCXHvj1WkTQ1/atCEzps3COcd3a78nukDg87Zm7ep87Z23M6bOomHj+gDsSNyRdt5+/+2PuNRUChUuxMEDB9m//wAABw8cZNni5ZQtf24WtjJrVaxaga2btvHnlu0cOnSIuZ/Pp26jOunK1L20Dl/N+BrnHD9++zP5o/NTNKYofx38iwNeX/118C9WLV1NmXLnBKMZWapClfJs27SN7Vt9fTb/i4XUaZTuaxF1Gtbi68/m4pzj529/IX90Pop6wd7dO/cAkPBnAovnLKVRi/pZ3gbJ+a6/6Rre/3As7384lsZNG/Hp1Jk45/h27XdER0dnCpKbGbXqXMTsz+cA8OknM2jUtCEAJc4szvIlvmT7HYk72bB+IyVLnZWl7QmmouXOJenP7eyLTyAlOZmNi5dRsmb1dGVav9GfNoMH0GbwAEpdXJOad91CqdoX8dfeJP7xroPJ//zD9u9+pOBZZwahFVnjupvap00OfWnThsxIO+6+P+pxV7N2DWZ/MQeAT6fO5NImDdPW70vax+oVa7i0SYOsbIaEqRyZKQTgDdeZhC8w9I63eD2+4MckoB0Q+R92fZ2ZjQXOBcoCPwOzgAfMbLZz7pCZVQS2BNj2K+AlM7vNOfeumUUArwBjnHMHMg0hgZeBN8zsOufcQfPdkasBvqFa4AsAXYsv8+VmYIGXJfWHt80HXuCjmnNu7THa9D7wlJld6Zz7FHzz6RylDbOAR8zsEeecM7Ma3hCpefjmOvrazM4HqgXYNgk4VbPgBqyHF0T73Tn3hve4Gr7MnHedc4f7DTObi68vj+Yn4FwzK+cN4bvpOPWZhy+75yV8AaKrgVvxZS/t8t7fykDd4zXMe82l+IagtcEXHMqWE8pc0vBilixYyo2tbyUqKoqn+j6Ztu7Jh56iW6/OxMTF8ECne+nd9XlGDhlNhcrlufJq352g5n45j5nTviB3ZG7y5DmDPgOeCflJBus2rMPSBUvp0OZ28kTloZvfPErdHnqaJ3s9QUxcDB073Uvfbv0YNWQMFSqV44qrWwK+L50db36IA/sPYGZMHjeFsVNGkj86P32792PNim/Ys3sP17a4iTsfuC2tr0NRRO4I7nziNl54fACpKY4mrRtRumwpvvhoNgDNr27K7h27efquXhzcfxDLlYvPJs5i4PsvkbQ7iVee8t0tLzUllfrNL6F63UCXrdBTr2FdFs1fwrVX3kxUVB56Ptc9bd3jD3bl6d5diY2L4aHH7+eZrn0Y/uYoKlYuT9v2vtssz/5iLlMmfUJERAR58uThuQG9MDN27txFt049Ad9dplq0asYlDS4OShuzQkTuCB548j56PtKblJRUWrS9jHPKnc2nk313V7zy2lbUrl+T5QtXcNdV9xMVlYfHez0CwK4du3nuyRcBX181vrwRtepddNTXChURuSO4r8vd9H60H6mpqVzWpglnly3NZ1N8v6O1at+CmvUvYsWi1dx/zSPkiTqDR555KG37/t0HsndPErlz56bjk/cQXTAagMVzlvL2wHfYs3svzz3+IudWLEOfN3oGpY3Zydie7WlY/RxiCuVj3aROPDdmDmNnrAl2tbKV+o0uYeH8xVzd6nqi8kbx7HNPp6177IHO9OzTndi4WB5+/AF6PNmLoYNHUKlKRdq1bw3A3fffQZ8e/bjx6ltxzvHw4w9SOEOmTCjLFRHBRXd0YO6Lr+FSUynbuAGFSpdknRfIKH+MuW7+2rWbpUNH4VIdzqVydt3anHVRaGaWZlS/4SUsmreE9lfcSFRUFM88/1Tauk4PPEmPPt2IjYvhkccfoEfX3gwbPJKKlSukfQ4DzPlqHhfXq03efHnT7btn196sXL6a3bv30Pqy9tz70F1px6scXy7dkj4gCzSeMTszs33OuWjvcXHgD2CAc6639/wTfMGUr/Bl90R72RxdnHOHJ46e4z1f4b/OzMYAu/Bl+xQHnnDOTTezXPjmgmmDLyCQgG8Omxr++/X2XRrfHD+VvXrM8Mr87c2xM905d75X1vDNL3MrvkySP4GHnXPfHm4r8BpwBbAHuME5l2Bm5+K7e9iZ+AJfE5xzfb36T3fOTQ7QV5XxZVeVAw4B3wCPAa2AWs65h71yeb1y9by2rvf6Ji8wGqgKrME3R9KjXh+u9/aRaL5Jt6sBn+Gb++lXYLvfW/g4cOXhembYthYw0DnX+Bj1eAq4xWvDn/iCZVOAl5xzM/3eh0fxzSE1McN7/yawwjk3xguMDQIS8d217HzvNe4A3gR2+9W7LnA9cJf3fKRzbpA3x9THQEl8AcRYoLdzbk6G/r8WaO2cu8MbelbBa9dXQCe/jKJMsnr4WKhJcanBrkKO9ueBbcGuQo5XJjr0M0ROp13/7A52FXK0f1L+CXYVcrwaV38c7CrkaNs/P+6NaeU4Bn77U7CrkKN1Pr9isKuQ4xU6Iy4koilTN3ye7f+uantOiyzv6xwXFAon/kEFCV8KCp0cBYVOjoJCJ09BoZOjoNDJUVDo5CkodHIUFDp5CgqdHAWFTp6CQlknGEGhHDt8TERERERERETkRIT4LBb/WU6eaDrkKUtIRERERERERE4XBYVERERERERERMKQho+JiIiIiIiISEgz3X0sIGUKiYiIiIiIiIiEIQWFRERERERERETCkIaPiYiIiIiIiEhI093HAlOmkIiIiIiIiIhIGFJQSEREREREREQkDCkoJCIiIiIiIiIShjSnkIiIiIiIiIiENN2SPjBlComIiIiIiIiIhCEFhUREREREREREwpCGj4mIiIiIiIhISNMt6QNTppCIiIiIiIiISBhSUEhEREREREREJAxp+JiIiIiIiIiIhDTdfSwwZQqJiIiIiIiIiIQhBYVERERERERERMKQho+JiIiIiIiISEhTRkxg6hcRERERERERkTCkoJCIiIiIiIiISBjS8DERERERERERCWlmuvtYIMoUEhEREREREREJQwoKiYiIiIiIiIiEIQ0fExEREREREZGQpsFjgSlTSEREREREREQkDCkoJCIiIiIiIiIShhQUEhEREREREREJQ5pTSERERERERERCmm5JH5gyhUREREREREREwpCCQiIiIiIiIiIiYUjDx0REREREREQkpGnwWGDKFBIRERERERERCUMKComIiIiIiIiIhCENHxPJ5uIPbg92FXK0InmKBbsKOVqZAmWCXYUc76wrRwW7Cjna7590CHYVcrS8ufMFuwo53vbPHwx2FXK04i3eCnYVcrxNn90T7CrkaCVaDg92FXK8g7OfCXYVTgndfSwwZQqJiIiIiIiIiIQhBYVERERERERERMKQho+JiIiIiIiISEjT4LHAlCkkIiIiIiIiIhKGFBQSEREREREREQlDGj4mIiIiIiIiIiHNNIAsIGUKiYiIiIiIiIiEIQWFRERERERERETCkIJCIiIiIiIiIiJhSHMKiYiIiIiIiEhIy6UphQJSppCIiIiIiIiISDZnZi3N7GczW2dm3QOs72Bm33j/FpnZhcfbp4JCIiIiIiIiIiLZmJlFAEOAVkBV4CYzq5qh2B/Apc65asBzwIjj7VfDx0REREREREQkpIXALenrAOucc78DmNkEoB3ww+ECzrlFfuWXAKWOt1NlComIiIiIiIiIBJmZ3WdmK/z+3ee3uiSwye/5Zm/Z0dwNfHa811SmkIiIiIiIiIhIkDnnRnD0IV+BUp1cwIJmTfAFhRoc7zUVFBIRERERERGRkGY5fvQYm4HSfs9LAVszFjKzasBIoJVzbsfxdqrhYyIiIiIiIiIi2dtyoIKZnWtmZwA3AlP9C5jZ2cAU4Fbn3C8nslNlComIiIiIiIiIZGPOuWQzexiYBUQA7zjnvjez+731w4BngWLAW+ZLjUp2ztU61n4VFBIRERERERGRkBYCdx/DOTcDmJFh2TC/x/cA9/ybfWr4mIiIiIiIiIhIGFJQSEREREREREQkDGn4mIiIiIiIiIiEtBC4+9hpoUwhEREREREREZEwpKCQiIiIiIiIiEgYUlBIRERERERERCQMaU4hEREREREREQlpoXBL+tNBmUIiIiIiIiIiImFIQSERERERERERkTCk4WMiIiIiIiIiEtJ0S/rAlCkkIiIiIiIiIhKGFBQSEREREREREQlDGj4mIiIiIiIiIiFNdx8LTJlCIiIiIiIiIiJhSEEhEREREREREZEwpOFjIiIiIiIiIhLSlBETmPpFRERERERERCQMKVNIRI5q9eK1vPPau6SmpnJZ2ya0v61tuvWb129hyPPD+f3n9dx8//W069AagMTtO3ijz1B279iN5TKaX9WU1je0CkYTsoRzjjcHDGXpwmVERUXRtU9nKlapkKncti1/8lz3F0jak0SFKuV56vmuREZGHnP7Ab1fYcm8pRQuWph3Jo9I29ecL+Yxdth7bPxjE2+99waVzquYZe09nZxzvPrSGyyev4Q8UXl45vmnqFy1UqZyWzdvpWfXPuzds5dKVSrS+8WeREZGArBy+WoG9R9McnIyhQsXYuiYwQA8/8xLLJy3iCJFi/D+R2OztF3B0rxWWQY+2JyIXMaYz9YycOLidOsLR0cxvPOVnHtWEf7+J5mOr3zKD+sTAHikfW3uaFUd5+D79fHc9/J0/j6UEoxmnHbOOQYPeIslC5cTFZWH7n26HOUc3kbf7i+wd08SFatU4Gm/c/ho23/wvw/59KOZYFC2/Ll069OFPHnOYNSQMSycuxgzo0jRwnTv8yQxccWyuumnXFpfLFjm64u+Tx69L7u9wN49e3192a8bkZGRbPhjI/17DeTXH9dx98N3cuPt1wHw99//8NhdT3Do0CFSklO4tFlD7nzw9qxuXpZzzvHKi4NYOH8xUVFR9OrXI+A1ccvmrfR4slfaNbHvS88SGRnJvqR9PNO9L9u3bSc5JZlb7riZtldfGYSWZE/DurahVd2KJOzeT627hgW7OtmCc45B/QezeMFSoqKi6PFcNypVyfwdY+vmbfTq1pe9e5OoWLkCz77wdNrnMMCP3/3Efbc+RN8Bz9Kk+aUAXNPqRvLly0euiFxERETwzvjhWdauYGleuxwDH77c9zk8YzUDxy9Kt75wdBTDu7bh3DOL8PehZDoOmJb2OfxQ+zrceWUNzIzRn67izQ+XBaMJEgaUKXQamZkzs1f8nncxs96naN9jzOzak9xHKTP7xMx+NbPfzOx1MzvDb/14M/vGzPab2Roz+8HMDnqP15jZtWY2w8wKn3SDjl7Hx8zsOzP73sw6+S0vamZfeHX/wsyKeMsbm9keM1ttZj+b2Twza32K69TYzKb/i/J3mNlZp7IOWSElJZW3B46mx2tdGTT+ZRZ8vohNf2xOV6ZAwWjufuJ22t6c/gtmREQu7ni0A29MHMhLI/syc/IXmbYNJUsXLGfLxi2898lonuj5GINeGByw3IjXR3Jth/a8N3U0BQpEM+Ojmcfd/vI2LXhpSL9M+zq3XBn6vPIs1S664PQ0KkgWz1/Cpg2b+eDT93mq15MMeP7VgOWGvDacm269nsmfjqdgwQJMnfIpAEl7k3j5+Vd5efCLjP/4Xfq90jdtmyvbteS1oS9nSTuyg1y5jEGPXE67pydS454RXNekKpXPjklXputN9Vj723bqdBzJ3QOmMfDB5gCcVSyaB6+qTf2HRlPrvreJyJWL65pUDUYzssTSBcvZvHEL4z4ZTeeenXjthTcClhv++iiu7dCecVPHEJ3hHA60fUJ8Ih+O/5jh495kzOS3SU1NZfasOQDcePt1vDNpOKMmDuOShhczdsT/sqStp9vSBct8fTF1DJ2f6cRr/Y7Sl4NGcu0t7Rk3bSzRBY/0ZcFCBXi060PccFv6rzhnnBHJq2+/zKhJwxk5cRjLFq3g+29+OO3tCbZF8xezceNmpsyYyNO9u/LScwMDlnvztaHcfOsNTJkxkYIFC/DJh76vKR+M/5Cy5crw/pSxDB/9Jq+/PJhDhw5lZROytfdmrqVdt3HBrka2snjBUjZv3MLEaf+j67OdGfj8awHLDX19ODfcch0Tp/2PAgULMP2jGWnrUlJSeGvQCOrUq51pu8EjX2PspJFhERDKlcsY9FhL2nV/nxp3DuW6pudT+ZwMn8Md6rN23Xbq3DuCu1/8hIEPXw5A1TKx3HllDRo+OIo69wynVd0KlCtZNBjNCClmlu3/BYOCQqfX30B7M4s5bsksZGYR5jvipgAfO+cqABWBaKCfV6YEUM85V805l985Vx24AvjNOVfd+zfZOXeFc273aarn+cC9QB3gQqC1mR3+ubE78JVX96+854fNd87VcM5VAh4F3jSzy05HHU/QHUCOCwqt+2EdJUoVp0TJ4kRG5qZB80tYPm9lujKFihaifNVy5M4dkW55kZgilK18LgB58+elVJmS7IzflWV1z2qL5i6meetmmBlVq1VhX9J+diTsSFfGOcfq5Wu5tFlDAFq0ac7COYuPu/2FNS+gYKECmV7znLJnc3aZ0qe5ZVlv3tcLuKLt5ZgZ5194HvuS9pGYkJiujHOOFctWpf3yeEXblsybPR+AWTO+pPFljShxZnEAihYrkrZdjVrVKVioYBa1JPhqVzqL37buYv2fuzmUnMoHc36gdb30GRuVz4lhzur1APyyaQfnFC9EXOH8AOSOyEXePLmJyGXkzZObbTv2ZXUTsszCuYu4vHVzzIzzjnEOr1q+hkubNQKgZZvmLJiz6Ljbp6Sk8Pfff5OcnMJff/1NTKzvS33+6Pxp+/7r4F9B+yJ4qi2cs5jLvevZedWqsi9p3wn0ZQsWfL0QgCJFi1D5/EpE5E6fzG5m5MuXF4Dk5GSSk5NDps+OZe7XC7iybUvMjAsuPJ+kpKSA18TlS1fStEVjAK5sdwVzZ8/zrTRj//4DOOc4cOAgBQsVJCIiAvFZ+M1Gdu49GOxqZCsLvl5IyzYtfJ/D1aqSlLSfxADn8Mplq2mc9jl8OfNmL0hbP3n8RzRu1pAiRQtnZdWzndqVz+K3LbtYv837HJ79Pa3rpc/0q3xOLHNW/QF4n8MlChFXJD+Vz4lh2Q9bOPh3MimpjvlrN9KuQeYsQZFTQUGh0ysZGAE8nnFFxkwfM9vn/d/YzOaa2SQz+8XMXjKzDma2zMy+NbNyfrtpZmbzvXKtve0jzOxlM1vuZfl09Nvv12b2PvAt0BT4yzk3GsA5l+LV8y4zywd8DsR5GUENj9ZAM1tvZjFmVsbMfjKzkV5mzzgza2ZmC71snjpe+fxm9o5Xv9Vm1s5bfp7XxjVevSsAVYAlzrkDzrlkYC5wtffS7YDD4z/GAlcFqp9zbg3QF3jYe502ZrbUe+0vzay4meXy6hjrlcllZuu8dl3ntWetmc07Wj942z3rtes7MxthPtcCtYBxXtvymllN7z1eaWazzOzMY+03WHYm7Eo3lKFoXFF2JOz81/uJ35rAH7+sp8L55Y5fOIdKjE8krkRs2vPY4jEkxqf/ArV3916iC+Qnwgug+coknvD24SIhPpG4EnFpz+OKx5IQn/4PoD2791CgQDS5vT8a40ocKbNpwyaS9ibxwJ2Pcvv19zBj6sysq3w2c1ZMATYn7E17viUxiZIx6QOM3/6+Pe1LZq1KZ3J28UKUjC3A1h37GDR5Kb+Me5g/Jj7G3v1/89XKP7K0/lkpIX4HsRnOwYQM5+Ce3XuJLhCdFgT3lUk85vaxcTHccNt1XN/qFq5pfiPR0fmofUmttHIj3xzNdS1v5ovPZnPXA7edziZmmYT4RGL9zmH/fjoscF8e/5qXkpLC3dd35Kqm11Gr7kVUvaDKqa18NpSwPYHi6a6JccRvT0hXJtM1sXgs8fG+MtfffA3rf19PqybtuOnq2+jcvRO5cunrvxxdQnwiccX9j7kTOYePfA4nbE9g3uz5XHVd+ikHAAzj8fuf5K4b7+OTydNOYyuyh7NiCrI53v9zeC8lYzN8Dv+2nXYNKwNQq/JZnF28MCVjCvD9Hwk0qHY2RQvmJW+e3LS8uDyl4sLnhy3JWvpUOP2GAB3MrNC/2OZC4DHgAuBWoKJzrg4wEnjEr1wZ4FLgSmCYmUUBdwN7nHO1gdrAvWZ2rle+DtDDOVcVOA9Il/bhnNsLbATKA205khU0/wTrXR54HagGVAZuBhoAXYCnvTI9gNle/ZoAL5tZfuB+4HUvI6kWsBn4DmhkZsW8QNUVwOHUiOLOuW1evbcBRz69Mlvl1QdgAVDXOVcDmAB0dc6lAv8DOnhlmgFrnXOJwLPA5c65C70+OZY3nXO1nXPnA3mB1s65ycAKoIPXtmRgMHCtc64m8A5edpY/M7vPzFaY2YoPxkw5zsueHs65TMuMf/er7MEDf/HyU69xZ6dbyZc/36mqWrYToKsy/YIdsD+9Mieyfbg4keMuYH95/6ckp/DTj7/w6pD+vD58IO8MH8vG9ZtOQ02zv0CHUMa+GzhhMYULRLFk2N08cFUt1q77k+SUVApHR9H6kgpUufUtyt74BvmjIrnxsvOypuLBEPD8PJEydsztk/YmsXDOIiZMf5cPPx/PwYN/8fmnX6aVuefhO/lg5vs0b9WUjyZOPakmZBvH6qdjljn+riMiIhg1aTgfzBrPj9/9zO/rQjdQedixPjtOpMyShcuoWLkCn339CeM+HMPLL7zKvn37T09lJSQ4jn9+HuuYe/3lITzQqWPAjLShYwczeuIIXhnSnykTP2bNyrWnptLZVODP4fR9N3D8Qt/n8Ih7eeDq2qz99U+SUxw/b0zklQmLmP5yB6b2v5lvfttOckpqFtU8lFkO+Jf1NNH0aeac22tm7+IbxnSi+anLDwc8zOw3fFk74MvwaeJXbpIX0PjVzH7HF/hoAVTzy0IqBFQA/gGWOecOf4MyCHDVP/ryE/GHc+5br97f4xve5czsW3wBLLz6tTWzLt7zKOBsYDHQw8xKAVOcc78CP5pZf+ALYB+wFl9Q5d/yP7tKARO97JwzgMP98Q7wCTAIuAsY7S1fCIwxs0n4htsdSxMz6wrkA4oC3wMZfwapBJwPfOF9eEYA2zLuyDk3Al+WGd/tWvlf34+TUiyuaLpslZ3xOykaW+QYW6SXnJzMy0+9RsPL61O3SZ3TUcWg+njiVD6d8hkAlc6rSPyfR365TdieSLHY9OO+CxUpxL6k/aQkpxCRO8Ir48vEii0ec9ztQ9nk8VPS5r+ocn5l4v+MT1sXvz0h0+S7hYsUIilpH8nJyeTOnZv4PxOIifON0o0rHkuhIoXImy8vefPlpUbNC/n153UhOdTueLYkJFEq9siviiVjCrB1R1K6MkkH/qHjwE/Tnv/03oOs/3M3zWuVZf2fu0nccwCAjxf8TN2qpZjw1fdZU/ks8NHEqUyf4psDo/J5lUjIcA7GxKY/7nzn8D6Sk1PI7Z3DMX7ncKDtVy5dzZlnlaCwN4SiUdMGfL/2B1pc2Szdvi9r1ZTuj/bkzhyaLfTRhE8y9OWRc/jf9uWJKFAwmuq1LmTZwhWULX/u8TfIYSaN/5CPJ/uChFXPr8L2dNfEeGLj0s9KULhI4fTXxO0JxMb6ykz76FNuv+cWzIzSZ5firJJnsuGPDZx3QejOESb/3ocTPkqbm6/KeZWJ3+5/zCUSE5vxmMt4DiekncM/ff8zvbr55vPbs2sPi+cvJSIigkZNG6Qdu0WKFaFR04b88N1PVK95YVY0MSi2JOxNl91TMqYgWxPTD8VOOvAPHQcc+XPhp/cfYf2fvikXxn62hrGfrQGgz91N2OKX/StyKilTKGsMwpfBk99vWTJe/5svOnCG37q//R6n+j1PJX0gL2OwwOELgDziN+/Puc65w0El/5+GvseXkZPGzAriy8T57cSalcmJ1NuAa/zqd7Zz7kfn3Pv4MnEOArPMrCmAc26Uc+4i51wjYCfwq7ef7YeHXXn/H/n0yqwG8KP3eDC+jJ4LgI74glI45zZ5+2wKXAx85i2/H+iJr1/WmFnAb61eltZb+DKALgDePrzvjEWB7/3af4FzrsUx6h405auUY9umP9m+NZ5Dh5JZ8MViajWseULbOud4q98ISpUpmWkS6lBx1Q1teXviUN6eOJQGTerxxfQvcc7xwzc/kj86X1rA5zAzo3qtC5n7pS/x7vNpX1C/8SUA1Lu07nG3D2XX3tSe9ya/w3uT3+HSpg2ZMXUWzjm+W/s90dH5M30ZNTNq1q7B11/MBWDG1Jk0bNIAgIZNG7B21TckJyfz18G/+P7bHylT9pwsb1N2sOLnrZQvWYRzShQiMncurmtclU8X/5quTKH8eYjM7fsqcGer6iz4dhNJB/5hU/xe6lQpSd48vkt3kxpl+HljaA1pvPqGtoyaOIxRE4fRoEk9Zk3/Aucc33/zI/mj8wc8h2vUupC5X/pGEs9Mdw5fEnD7uBKx/PDtT/x18C/fPDrLVnPOuWcDsHnDlrR9L5q7OEcHLq++sR2jJg1n1KThNGhSn1ne9ez7b344wb78nPqN6x3zNXbv3E3SXt8fU3//9Tcrl67i7HNzbp8dy/U3XcP7H47l/Q/H0rhpIz6dOhPnHN+u/Y7o6OiA18RadS5i9udzAPj0kxk0auob+V/izOIsX+JLDN+RuJMN6zdSslSOm+ZQTrNrbryasZNGMnbSSBo1qc/MaZ/7Poe/+cH7HM58Dl9UuwZz0j6HZ9GwSX0AJn82ng8/m8CHn02gcfNL6dKjE42aNuDggYPs3+/7oeHggYMsWxyaQV1/K37aSvmSRTmnRGHf53DT8/h08S/pyqT7HL6yBgu+2UjSgX8AiC3sy7IvHVeQdg0rM2l26PwwI9mLMoWygHNup5dpcje+jBSA9UBNYBK++XEiA299TNeZ2VjgXKAs8DMwC3jAzGY75w6ZWUVgS4BtvwJeMrPbnHPvmlkE8Aowxjl34DQOXZkFPGJmj3hZRDWcc6vNrCzwu3PuDe9xNWC2mcU55+LN7GygPXCJt5+pwO3AS97/nwR6MTOrBjwD3OMtKsSR/sh4L9uR+IaRvefNsYSZlXPOLQWWmlkbjgxfy+hwACjRzKKBa4HJ3rIk4PAA4p+BWDO7xDm32Mwi8Q0PzHZX+YjcEdzT5Q6ee+wlUlNTadq6MWeXLcWsKb6hD5e3b8auHbvpekdPDu4/iOUypk+YyesTBrDh143M/WwBZ5crTedbnwLg5geup2a9GsFs0mlzcYM6LF2wnFva3klUVB669u6ctq77wz3p8uzjxMQV477H7ua57i/wzltjKF+pPK2uuvy42z/X/UXWrvyGPbv3cP3lHbjj/lu54uqWzJ+9kMH932LPrj08/egzlKtUjgFvvZDlbT/V6jWsy6J5i7n2ipuIispDz+efSlv3+ANP8nSfbsTGxfDQ4/fzTNfeDB88koqVK9C2vS/4eG7ZMtStfzG3XHMnuXLlom37KylXoSwAz3Ttw6rlq9m9ew9tLruGex+6k7btT+nNCbOVlFTH429+zrQXbyQiVy7GzlrLjxsSuae17zwcOX01lc+OYWS3NqSkOH7amMj9r/h+KV7+01Y+mv8Ti9+6m+SUVNb+9iejZqwOZnNOq7oN6rB0wTI6tL2DPFF56Na7S9q6bg/34MlnnyAmrhgdH7uHvt1fYNRbY6lQqRxXXNXymNtXvaAKlzZryL03P0hERAQVKpen9TVXADDijVFs3LCJXLlyUfzMOJ7o8VjWN/w0qNuwDksXLKVDm9t9fdHHry8feponez1BTFwMHTvdS99u/Rg1ZIyvL6/29eWOxJ10vPkhDuw/gJkxedwUxk4ZyY7Enbz4zABSU1NJTXU0adGIeo3qBquZWaZ+o0tYOH8xV7e6nqi8UTz73NNp6x57oDM9+3QnNi6Whx9/gB5P9mLo4BFUqlKRdt617e7776BPj37cePWtOOd4+PEHKVykcJBak/2M7dmehtXPIaZQPtZN6sRzY+YwdsaaYFcrqC5pWJfFC5ZyfetbiIrKw9N9u6Wt6/xQd7r36kJsXAwPdLqPXl2fY8SQUVSsXIHWV19xzP3u3LmLpx9/BoDk5BRaXNGMuvVDL5PcX0qq4/HBM5nW/2YiIoyxn63lx/UJ3NPmIgBGTltF5XNiGNm9HSmpjp82JHL/y0eyhsb3vo6iBfNyKCWVTq9/xu59fwWrKRLiLNCYUDk1zGyfcy7ae1wc31ClAc653t7zT/BlC32FL7sn2swaA12cc4cnjp7jPV/hv87MxgC78GX7FAeecM5NN7NcwPNAG3xZKQn4JmGu4b9fb9+l8WW3VPbqMcMr87eZlQGme/PjHC4faNl6rw7R/uu8+k13zk32387M8uLLnKrn1W+9156ngFuAQ8CfwM1eMG0+UMxb/oRz7itv/8XwBdTOxjcP0nVe+cZev/6ObxhXvNfn07zt2gGv4QsMLQFqO+cae+sigR1AHefcT96yKfiG35n3PnXCN4/TZ17Zw67DN7fTjfgCfpuADd57fQ3wAr4sqEvwDSF7A1+AKjcwyDn3NkcRrOFjoaJInvDJuDkd8uXOG+wq5HhnXTkq2FXI0X7/pMPxC8nRhen8ZKdS/tyhOydeVije4q1gVyHH2/TZPccvJEdV+gp9Dp+sg7OfCYkPk1WJi7P931UXxVyS5X2toJCIx8xqAa855456t7VgUFDo5CgodHIUFDp5CgqdHAWFTpKCQidNQaGTo6DQyVNQ6OQoKHTyFBTKOsEICmn4mAhgZt2BBzhyBzIRERERERGRkKagkAjgnHsJ3/xEIiIiIiIiEmJO47y5OZruPiYiIiIiIiIiEoYUFBIRERERERERCUMaPiYiIiIiIiIiIU7DxwJRppCIiIiIiIiISBhSUEhEREREREREJAxp+JiIiIiIiIiIhDQNHgtMmUIiIiIiIiIiImFIQSERERERERERkTCk4WMiIiIiIiIiEtJMA8gCUqaQiIiIiIiIiEgYUlBIRERERERERCQMKSgkIiIiIiIiIhKGNKeQiIiIiIiIiIQ205xCgShTSEREREREREQkDCkoJCIiIiIiIiIShjR8TERERERERERCmgaPBaZMIRERERERERGRMKSgkIiIiIiIiIhIGNLwMREREREREREJcRpAFogyhUREREREREREwpCCQiIiIiIiIiIiYUjDx0REREREREQkpJmGjwWkTCERERERERERkTCkoJCIiIiIiIiISBjS8DERERERERERCWmm0WMBKVNIRERERERERCQMKSgkIiIiIiIiIhKGNHxMREREREREREKcxo8FokwhEREREREREZEwpKCQiIiIiIiIiEgYUlBIRERERERERCQMaU4hEREREREREQlppjmFAlJQSCSbK5YnJthVyNFSXWqwq5Cj7fl7b7CrkONtmHZbsKuQo20/sD3YVcjRHC7YVcjxhq7/K9hVyNE2fXZPsKuQ45VuNTLYVcjRdAyKHJuGj4mIiIiIiIiIhCFlComIiIiIiIhISNPgscCUKSQiIiIiIiIiEoYUFBIRERERERERCUMaPiYiIiIiIiIioc00gCwQZQqJiIiIiIiIiIQhBYVERERERERERMKQho+JiIiIiIiISEgz3X8sIGUKiYiIiIiIiIiEIQWFRERERERERETCkIaPiYiIiIiIiEhI0/CxwJQpJCIiIiIiIiIShhQUEhEREREREREJQwoKiYiIiIiIiIiEIQWFRERERERERETCkIJCIiIiIiIiIiJhSEEhEREREREREZEwpFvSi4iIiIiIiEhIM9Mt6QNRppCIiIiIiIiISBhSUEhEREREREREJAxp+JiIiIiIiIiIhDgNHwtEmUIiIiIiIiIiImFIQSERERERERERkTCk4WMiIiIiIiIiEtI0eCwwZQqJiIiIiIiIiIQhBYVERERERERERMKQho+JiIiIiIiISEgzDSALSJlCIiIiIiIiIiJhSEEhEREREREREZEwpKCQiIiIiIiIiEgY0pxCIiIiIiIiIhLaTHMKBaJMIRERERERERGRMKSgkIiIiIiIiIhIGNLwMREREREREREJaRo8FpgyhUREREREREREwpCCQiIiIiIiIiIiYUjDx0QkHeccgwe8xZKFy4mKykP3Pl2oWKVCpnLbtmyjb/cX2LsniYpVKvD0812JjIw86vYb12+iT7d+ftv/yZ0P3MZ1HdpnZfNOi2ULV/DmwKGkpqRyxdUtufnOG9Ktd87x5stDWbrA1ydd+3RO69NjbTtlwid8PHEqERER1G1Qh46d7mHFklW8/cY7JCcnkzt3bjp2uoeL6lTPyuaeVssXrWTYwBGkpKbS6qoW3HDHdenWO+cYOnAEyxauICoqD517d6JC5fL88/c/dL63G4cOHSIlJZWGl9Xnto4dABg79D0Wz12K5TIKFylMl96dKBZbLBjNyxLOOV7vP4QlC5aSJyoPTz/XlUpVKmYqt3XzNnp3e56kvUlUrFyBni90JzIykvlfL2TkkNHkypWLiIgIHn3yQapddAEALz77MovmLaFI0cK8O2VUVjcty61evJbRg94lNSWVy9o24erb2qZbv2X9Fob0G87vP6/npo7X065DawASt+9gcN+h7N6xG8tlNG/XlCtvaBWMJgSdrw/f8/qwcYA+3MqQfsP5w+vDth2uBHx9+GbfoezesQfLZTRr15Qrb2gZjCYE1bY137L63fG4VEfZJg2p0u6KgOV2/PYHXz3Tj0seu5/SF9ci5Z9DzO7bn5RDh3ApqZS+uCbnX3dV1lY+SJxzDOo/mMULlhIVFUWP57od9RrYq1tf9nrXwGdfeJrIyMi09T9+9xP33foQfQc8S5PmlwJwTasbyZcvH7kifNfHd8YPz7J2ZUfDurahVd2KJOzeT627hgW7OtnS6Twe5d8zDSALSJlCfszMmdkrfs+7mFnvU7TvMWZ27UnuI8XM1pjZd2Y2zcwK/8f93GFmb3qPe5vZFm+/h/8F3K+ZFTazB09g/ydUziu7719V/hQws0Jm9q6Z/eb9e9fMCh2jfKY6mtn9Znbb6a1pcCxdsJzNG7cw7pPRdO7ZiddeeCNgueGvj+LaDu0ZN3UM0QWimfHRzGNuf3aZ0oyaOIxRE4cx4v0h5InKQ8Mm9bOsXadLSkoKr/cfwkuDn2f0hyOYPXMO63/fkK7M0oXL2bJxK+998g5P9HyMQS++edxtVy9fy6I5ixk5cSijJ4/g+tt8l49ChQvS7/U+jJo0jO59u/DiMy9nbYNPo5SUFIb0H8rzb/Th7Q/e4utZc9nw+8Z0ZZYvXMGWTVsZ/dEIHuvxMINffAuAyDMiGTDsBYaNf5Oh77/BikUr+fHbnwC49tZrGDbhTYa+P5iLG9bmf2+Pz/K2ZaUlC5axeeNmxk97l67PPsErz78esNyw19/m+luuYfy0dylQMJrpH30GQM2LL2LMB28zetIIuvfpQv8+aR+LtGp3OQOHvpgl7Qi2lJRURr4ymh6vduW18S+z4ItFbPpjc7oy0QWjuevx22l785XplkdE5OL2Rzvw+oSBvPh2X2Z++EWmbcNBSkoqo14Z4/XhABZ+sThAH+bnrsdvo02APrzt0Q4MmvAyL7zdh1lh2IepqamsHD2ORt0ep+XA59iwaCl7Nm8NWO6b9ydT4sLz05blisxN455daNm/D5e/1Itta78j8dffsrL6QbN4wVI2b9zCxGn/o+uznRn4/GsByw19fTg33HIdE6f9jwIFCzD9oxlp61JSUnhr0Ajq1KudabvBI19j7KSRYR8QAnhv5lradRsX7Gpka6f7eBQ5FRQUSu9voL2ZxQS7Iv7MLMJ7eNA5V905dz6wE3joFL3Ea95+D//bfZRyhYETCfacaLlgGQX87pwr55wrB/wBjMxYyK/fM3HODXPOvXsa6xg0C+cu4vLWzTEzzqtWhX1J+9mRsCNdGeccq5av4dJmjQBo2aY5C+YsOuHtVy1bTclSZ1LirOJZ06jT6KfvfqZkqTM5q9SZREZG0vTyS1k0Z3G6MovmLKZ568swM6pWq8K+pH3sSNhxzG2nTp7OTXdezxlnnAFAkaKFAahQuTwxXpZLmXLncOiff/jnn3+yrsGn0c/f/8JZpc/kzFIliIyMpHGLRiyeuyRdmcVzl9LsiqaYGVUuqMz+pP3sSNyJmZE3X14AkpOTSUlOwcz3a1D+6Hxp2/918K+05aFqwdcLadmmhXcOVmVf0j4SA53Dy1bT2Pu1sWXbFsyfvRCAfPnypvVRxv6qXrMaBQsWzKKWBNe6H9ZRolRxipcsTmRkbuo3u4Tl81amK1OoaCHKVy1HRO70HxdFYopQttK5AOTNn5eSZUqyM2FXltU9u1j3w29eH8Z5fViXFUfpw9zH7cOzwq4Pd677nQIl4oguHktE7tycfUkdtqxYnancrzO/otTFNclTsEDaMjMjMioKgNSUFFJTUkL+2neY/zXw/GpVSUraH/AauNLvGnhF28uZN3tB2vrJ4z+icbOGaZ+9EtjCbzayc+/BYFcjW9PxKDmBgkLpJQMjgMczrsiY6XM4e8TMGpvZXDObZGa/mNlLZtbBzJaZ2bdmVs5vN83MbL5XrrW3fYSZvWxmy83sGzPr6Lffr83sfeDbAHVdDJT0ypYzs5lmttLbf2VveRszW2pmq83sSzM74b/Azew8rw1rvHpVAF4CynnLXjazaDP7ysxWeW1t522erpy3vyf92tjnOK9d3cyWeGU/MrMi3vJ7vX2sNbMPzSyf33vzhpktMrPfD79PZnammc3zy65qaGblgZrAc34v2Reo5fXj8fr9cB17m1kX7/EcM+vv9dcvZtbQW3609zZTvU70fckKCfE7iC0Rm/Y8tngMCfHpP7z27N5LdIHotC/xvjKJJ7z97FlzadqyyelqQpZKTNhBnF97Y+Iytzcxfgdxxf36JC6WxIQdx9x284YtfLvqex687TE63fMkP33/c6bXnvfVAspXKpcWOMrpdsTvILZ4+v5IzNiXCTuILXEkbh9TvBg7vDIpKSk8cPMj3ND8FmpcXJ3K51dKKzd6yLt0uPIOZn82h9vuv+U0tyS4EuIT0x9vxWNJ9M7PwzKfw+nLzPtqAR3a3UHXh3vQvU+XrKl4NrMzYRcxcUeGGRaLK8rOhJ3/ej/x2xJY/8t6KpxX7viFQ8zOhJ0U8+vDonFF2fEfAjvx2xL445cNYdeHB3ftJm+xomnP8xUrwsFdu9OVObBzF1uWr6Jcs8aZtk9NTWVW99580vFxSlxQlWLly57mGmcPvmtgXNrzOL/vKIcFugamfY/ZnsC82fO56rr0Qx3BN/Tk8fuf5K4b7+OTydNOYyskVJzO41H+C8sB/7KegkKZDQE6HGs4UQAXAo8BFwC3AhWdc3XwZZ884leuDHApcCUwzMyigLuBPc652kBt4F4zO9crXwfo4Zyr6v9iXgbLZcBUb9EI4BHnXE2gC/CWt3wBUNc5VwOYAHQ9Sv0ftyNDx772lt0PvO6cqw7UAjYD3YHfvGyiJ4G/gKudcxcBTYBXzPczVLpyZtYCqOC1pzpQ08waHaM/3wW6Oeeq4QvM9PKWT3HO1XbOXQj86PXdYWcCDYDW+IJSADcDs7w2XAisAaoCa5xzKYc39B6vAc7zFgXs9+PI7b3nnfzqe7T3NlC90jGz+8xshZmt+N877/+LapwCzmValOnHxYBl7IS2P3ToEAvnLqZx82MdAjmHO1ZfHC5DgDLYMbdNSUkhKSmJIWMH0bHTPfTt9kK68n/8tp4Rb7zD4z0ePdkmZBuZeyNzXx7r2IuIiGDo+4MZN2MMP3//C+vXrU8rc+dDtzHu0zE0bdWYqZOmn8JaZz8n0o/HO24bXdaAcZ+M4YVBfRk5ZMwprmHOcCLn9vEcPPAXA596jTs63Uq+/PmOv0GoCXAw/rc+HMSd4diHAY7BjFa/O55qN19LrlyZv9LnypWLy1/qTZshA9n52x/s3hQew+8CfuZm+ig5+vn9+stDeKBTRyIiMieMDx07mNETR/DKkP5Mmfgxa1auPTWVlpB1Oo9HkVNFE01n4Jzba2bvAo8CJ5oPudw5tw3AzH4DPveWf4svWHLYJOdcKvCrmf0OVAZaANX8spAK4Qug/AMsc8794bd9XjNbgy+4tBL4wsyigXrAB35ftPJ4/5cCJprZmcAZ+IZJBfKac25ghmWLgR5mVgpfMObXAF/kDHjBC/Ck4stcCpSN1ML7dzjnOdpr47xMO/QF4wo75+Z6i8YCH3iPzzez5/ENT4sGZvlt+rHXtz/4ZUQtB94xs0hv/RovaBXwbya/5Rn7/URM8f5fie/9gaO/t5nqlXFnzrkR+IJ9bDuw4fjfCk/SRxOnMn2Kb+xy5fMqkfBnQtq6hO2JacOVDitUpBD7kvaRnJxC7twR6crEFo855vZLFyynYuXyFC1W5HQ2KcvExsUQ79fexPhEYmKLZi6z3a9P4hMoFluUQ4cOHXXb2LgYGjat7xsmdX4lLFcu9uzeQ+EihUnYnkCvzs/xVN8ulCx91mluYdaJiStGwvb0/VEsQ1/GxMWQ8OeRX9gSt++gaIYy0QWiubDmBSxfvIoy5cukW9ekZWOeeax32iTUoWLKhI+Z5ncOpzvetidkmli7cKZzOHMZ8A0X27ppK7t37aFwkX/zW0nOVyyuaLpMtR3xOykSc+LXreTkZAY+/RoNL69P3cZ1TkcVs72icUXTMvkAdsbvpGhM4RPePjk5mVeeHkTDy+tzcePwm0sjb9EiHNxxJDvtwI5d5C1SOF2ZXb9vYPEbvrlt/knax7Y132K5clGq9kVpZc7In4/YKpX4c+13FC5dKkvqntU+nPARU6d8CkCV8yoTvz0+bV389kRiYtPPDBHoGnj4u8pP3/9Mr259Adizaw+L5y8lIiKCRk0bEBvn20+RYkVo1LQhP3z3E9VrXpgVTZQcJKuOR5FTRZlCgQ3Cl+WR329ZMl5/eYEF//Eaf/s9TvV7nkr6wFvGP+4dvmDEI37z+ZzrnDscVNqfofxBL7vkHO/1H/LqtDvDnEBVvPKDgTedcxcAHYGo47b8cMWcex9oiy8wNsvMmgYo1gGIBWp69dp+lNcw4EW/+pV3zv2X29aMAR722tMnw2v5vwfmtWEe0AjYArxnvomhvwdqmFnase89Ppx9BJn7/UQcfv0UjrznAd/bo9QrqK6+oW3aJNANmtRj1vQvcM7x/Tc/kj86f6Y/Fs2MGrUuZO6XvrjezGlfUL/xJQDUu/SSY27/1cyvuSxEho6B7w/wLZu2sm3Lnxw6dIjZs+ZyyaV105Wpd2ldvpj+Fc45fvDrk2NtW79JPVYv9/0CuWnDZpIPHaJQYd+XhqcefZZ7HrmT86ufl6k+OVmlqhXZsmkrf3r9MefzedRtdHG6MnUvvZgvZ8zGOceP3/5Evuh8FIspyu5de9iX5JsT/u+//mbVsjWULuP742fLxi1p2y+ZuzRteShpf+NVjJ40gtGTRtCwSX1mTvvcOwd/IDo6f6bArplRo3Z15nzhi7/PnPo5DZvUA2Dzxi1pv1r+/OMvHDp0iEKFw2MeIX/lq5Rj26Y/2b41nkOHkln45WJqN6x5Qts653ir3whKnVOSNjddefwNQlT5KmUz9OESav2LPhza721KnlOSNjcFvuNWqCta7lyS/tzOvvgEUpKT2bh4GSVrVk9XpvUb/WkzeABtBg+g1MU1qXnXLZSqfRF/7U3in/0HAEj+5x+2f/cjBc86MwityBrX3Hg1YyeNZOykkTTyuwZ+d4xr4EW1a6RdA2dMnZV284vJn43nw88m8OFnE2jc/FK69OhEo6YNOHjgIPu9Pj144CDLFq+gbPlzEckoK45H+W/Msv+/YFCmUADOuZ1mNglfYOgdb/F6fHPRTALaAZGBtz6m68xsLHAuUBb4GV+2ywNmNts5d8jMKuILFhyrfnvM7FHgE2Ao8IeZXeec+8ALWFVzzq3Fl5lyeF+3/5uKmllZfJMxv+E9rgasBQr4FSsExHv1boIvWAWQlKHcLOA5MxvnnNtnZiWBQ865eDLw2rbLzBo65+bjG453OGuoALDNy7DpwHH6yczOAbY45942s/zARc65d81sNdAT31xCeI9XOefWeZlRp8rR3tuYjPXCN2QuW6jboA5LFyyjQ9s7yBOVh269j8wn0u3hHjz57BPExBWj42P30Lf7C4x6aywVKpXjiqtaHnf7vw7+xcqlq+jcs1NWN+u0icgdwSPdHqTbQz18t1Fv24Jzy5Vh6mTfL0Rtr72SixvUYemC5dzS7i7fLel7P3HMbQFatWvBy71f5a7rOpI7Mjfd+nTBzPho4lS2btrKe2+/z3tv+4YWDnjrhZCYfDAidwQPPXk/Tz/yLKkpqbRo25wy5c5h+mRfBkzra6+gTv1aLF+4gjuvupc8UXno3KsTADsTdzKw12ukpqaSmppKo+YNqdvQl50xavBYNm/YTK5cuYg7M5ZHnzpVc/RnT5c0vJglC5ZyY+tbiYqK4qm+T6ate/Khp+jWqzMxcTE80Oleend9npFDRlOhcnmuvNp3y/S5X85j5rQvyB2Zmzx5zqDPgGfS0th7d3ue1SvWsmf3Hto3v4G7Hrid1u1D8w/2iNwR3NP5Dp7v9BKpqak0bd2Y0mVLMWvKlwBc3r4Zu3bsptudPTm4/yCWy/h04kwGjR/AhnUbmTdzAWeXK02X254C4Ob7r+eiejWC2aQsF5E7grs730G/Tv1JTU2lSetLKV22FJ97fdjC68PuaX2Yi08nfsZr4wewYd2mAH14AxfVqx7EFmWtXBERXHRHB+a++BouNZWyjRtQqHRJ1n0xB4DyzRsfddu/du1m6dBRuFSHc6mcXbc2Z10UHhktlzSsy+IFS7m+9S1EReXh6b7d0tZ1fqg73Xt1ITYuhgc63Uevrs8xYsgoKlauQOurj30t27lzF08//gwAyckptLiiGXXrh2cW4GFje7anYfVziCmUj3WTOvHcmDmMnbEm2NXKVk7X8ShyKlmgMYzhysz2OeeivcfF8Q23GuCc6+09/wRfZs5X+DJAos2sMdDFOXd44ug53vMV/uvMbAywC9/8PMWBJ5xz070sleeBNvgySxKAq4Aa/vvNWD/v+TR8QaoF+IJDZ+ILVk1wzvU138TPr+ELRCwBajvnGpvZHUAt59zDZtYbuNd73cOuAm4CbgEOAX8CN3vBsvfxBYg+A/oD07zXXAPUB1o559b7l/PmFXoMuMfb/z7gFufcb2aWCvjfX/VVYDYwDMgH/A7c6ZzbZWYP4JsXaQO+oXkFnHN3eH073Tk32b+fzOx24EmvDfuA25xzf5hv4urBQF2vzxfjy0DanfH99PYXqI4FgX3OuYEZ3vMYYIVzrswx3turAtWLo8iK4WOhLNWlBrsKOdqh1ORgVyHHyxeZN9hVyNG2H9ge7CrkaIHms5B/Z/L6v4JdhRzt0arhMcH16VS6Vaab5Mq/sOmze45fSI4pJuqsIOWwnFq/J/2U7T8UyxaonOV9raCQSDanoNDJUVDo5CgodPIUFDo5CgqdHAWFTp6CQidHQaGTp6DQyVFQ6OSFTlDo52z/oVi2QKUs72vNKSQiIiIiIiIiEoYUFBIRERERERERCUMKComIiIiIiIiIhCHdfUxEREREREREQpoRElMjnXLKFBIRERERERERCUMKComIiIiIiIiIhCENHxMRERERERGRkKbhY4EpU0hEREREREREJAwpKCQiIiIiIiIiEoY0fExEREREREREQptGjwWkTCERERERERERkTCkoJCIiIiIiIiISBjS8DERERERERERCWm6+1hgyhQSEREREREREQlDCgqJiIiIiIiIiIQhDR8TERERERERkZCm4WOBKVNIRERERERERCQMKSgkIiIiIiIiIhKGFBQSEREREREREQlDmlNIREREREREREKbphQKSJlCIiIiIiIiIiJhSEEhEREREREREZEwpOFjIiIiIiIiIhLSdEv6wJQpJCIiIiIiIiIShhQUEhEREREREREJQxo+JiIiIiIiIiIhTcPHAlOmkIiIiIiIiIhIGFJQSEREREREREQkDGn4mIiIiIiIiIiENA0eC0yZQiIiIiIiIiIi2ZyZtTSzn81snZl1D7DezOwNb/03ZnbR8fapoJCIiIiIiIiISDZmZhHAEKAVUBW4ycyqZijWCqjg/bsPGHq8/Wr4mIiIiIiIiIiENsvxA8jqAOucc78DmNkEoB3wg1+ZdsC7zjkHLDGzwmZ2pnNu29F2qkwhEREREREREZEgM7P7zGyF37/7/FaXBDb5Pd/sLeNflklHmUIiIiIiIiIiIkHmnBsBjDjK6kCpTu4/lElHQSERERERERERCWmW8+8/thko7fe8FLD1P5RJR0EhkWxu1z+7gl2FHK3QGYWDXYUcrdAZBYJdhRzvrCtHBbsKOdrvU28JdhVytpw/f0LQdT4/KthVyNFKtBwe7CrkeJs+uyfYVcjRSrcaGewq5HgHv3422FUQn+VABTM7F9gC3AjcnKHMVOBhb76hi4E9x5pPCBQUEhERERERERHJ1pxzyWb2MDALiADecc59b2b3e+uHATOAK4B1wAHgzuPtV0EhEREREREREZFszjk3A1/gx3/ZML/HDnjo3+xTQSERERERERERCWkaUB2YbkkvIiIiIiIiIhKGFBQSEREREREREQlDGj4mIiIiIiIiIqFNd+QMSJlCIiIiIiIiIiJhSEEhEREREREREZEwpOFjIiIiIiIiIhLSTPcfC0iZQiIiIiIiIiIiYUhBIRERERERERGRMKThYyIiIiIiIiIS0jR4LDBlComIiIiIiIiIhCEFhUREREREREREwpCGj4mIiIiIiIhISNPdxwJTppCIiIiIiIiISBhSUEhEREREREREJAwpKCQiIiIiIiIiEoY0p5CIiIiIiIiIhDZNKRSQMoVERERERERERMKQgkIiIiIiIiIiImFIw8dEREREREREJKTplvSBKVNIRERERERERCQMKSgkIiIiIiIiIhKGNHxMREREREREREKaho8FpkwhEREREREREZEwpKCQiIiIiIiIiEgYUlBIRERERERERCQMKSgkIiIiIiIiIhKGFBQSEREREREREQlDuvuYiIiIiIiIiIQ0M919LBBlComIiIiIiIiIhCEFhUREREREREREwpCCQiIiIiIiIiIiYUhzComIiIiIiIhISDM0p1AgyhQSEREREREREQlDyhQSkROyavEaRr06htTUVJq1bco1t1+Vbv3m9VsY/NxQfv/5DzrcfyNX3dImOBUNMuccbw4YytKFy4iKiqJrn85UrFIhU7ltW/7kue4vkLQniQpVyvPU812JjIw85vYDer/CknlLKVy0MO9MHpHVTcsSzjle7f8Gi+cvJU9UHp557ikqV62YqdzWzdvo2bUPe/fupVKVivR+oQeRkZGsXL6aro/14KySZwLQ+LKG3H3/HWz4YyM9u/ZJ237L5q3c9+Bd3HjrdVnWtmBoXrssAx9sQUQuY8xnaxg4YXG69YWjoxjepTXnnlWYv/9JoePA6fywPgGAh66uzZ1XVMfMGD1jNW9OWR6MJmQJ5xyDB7zFkoXLiYrKQ/c+XY5y3m6jb/cX2LsniYpVKvC033kbaPuN6zfRp1s/v+3/5M4HbuO6Du0ZPexdPp3yGYWKFALg3ofvom7DOlnW5tMlrS8WLPP1Rd8nj96X3V5g7569vr7s143IyEg2/LGR/r0G8uuP67j74Tu58fYj52j/XgNZ7F0Dx3z4dlY2K0s553jlpddZNH8JUVF5ePb5p6lctVKmcls2b6Vn197s3ZNEpSoV6fNiTyIjI3lv9PvM/PQLAFJSUlj/+wZmzZtGoUIFee6ZF1kwbxFFihZhwkfvZnXTslzz2uUY+PDlvmvgjNUMHL8o3frC0VEM79qGc88swt+Hkuk4YNqRa2D7Otx5ZQ3fNfDTVbz54bJgNCHonHMM6j+YxQuWEhUVRY/nulGpSuDP5V7d+rJ3bxIVK1fg2ReeJjIyMm39j9/9xH23PkTfAc/SpPmlWdmEbGtY1za0qluRhN37qXXXsGBXR8LYcTOFzMyZ2St+z7uYWe9T8eJmNsbMrj3JfZQys0/M7Fcz+83MXjezM/zWjzezb8xsv5mtMbMfzOyg93iNmV1rZjPMrPBJN8j3etXMbLGZfW9m35pZlLe8pvd8nZm9YUe5H56Z3eSV+8bMZppZjLf8HDP7yls+x8xKecvLeO1ZbWY/mtkyM7v9VLTFr06HX+Nw/w0zs3+VZWZm1c3sCr/nd5hZglfvX81slpnVO8k6OjN7xG/Zm2Z2x3G2u8PMzvIetzOzj/3WPWVm6/yetzGzqf+1jjlZSkoqI15+h2cGPcUbE15lwecL2fT75nRlogtGc0/nO2jXITyDQYctXbCcLRu38N4no3mi52MMemFwwHIjXh/JtR3a897U0RQoEM2Mj2Yed/vL27TgpSH9Au4vVCxesJRNGzbzwfRxPPVsFwY8/2rAckMGDeOmW69j8vT3KViwAFOnfJq2rvpF1Xjvg1G898Eo7r7/DgDOOffstGVjJowgKiqKSy9rmBVNCppcuYxBj7Sk3dMTqHH3cK5rch6Vz45JV6brzfVY+9t26tw3krv7T2Xgg80BqFomljuvqE7Dh0dT5763aVW3AuVKFglGM7LE0gXL2bxxC+M+GU3nnp147YU3ApYb/vooru3QnnFTxxCd4bwNtP3ZZUozauIwRk0cxoj3h5AnKg8Nm9RP29+1t7RPWx8KASGApQuW+fpi6hg6P9OJ1/odpS8HjeTaW9ozbtpYogse6cuChQrwaNeHuOG2zF8PW7ZtwYC3Xjit9c8OFs1fwqYNm/nw0/E81asr/Z9/JWC5N18bxk23Xs+Hn46nQMECfDJlOgC33nkz4yaPZtzk0Tz0WEdq1KpOoUIFAbiyXSteHzowy9oSTLlyGYMea0m77u9T486hXNf0fCqfk+Ea2KE+a9dtp869I7j7xU8Y+PDlgHcNvLIGDR8cRZ17hnvXwKLBaEbQLV6wlM0btzBx2v/o+mxnBj7/WsByQ18fzg23XMfEaf+jQMECTP9oRtq6lJQU3ho0gjr1amdVtXOE92aupV23ccGuRlixHPAvGE7kD/u/gfaHgxPZhZlFeIGVKcDHzrkKQEUgGujnlSkB1HPOVXPO5XfOVQeuAH5zzlX3/k12zl3hnNt9CuqUG/gfcL9z7jygMXDIWz0UuA+o4P1reZTtXweaOOeqAd8AD3urBwLvesv7Ai/6bfqbc66Gc64KcCPwuJndebLtyeA3r/+qAVWBq/7l9tXx9b2/iV69KwAvAVPMrMpJ1DEeeMw/KHgC7gDO8h4vAi7xW3cJsNfM4rzn9YCFJ7JT770MGb/+sI4zSxWnRMniREbmpkHzeiyblz5roHDRQlSoWp7cuSOCVMvsYdHcxTRv3Qwzo2q1KuxL2s+OhB3pyjjnWL18LZc28wUlWrRpzsI5i4+7/YU1L6BgoQJZ26AsNu/rBVzR5nLMjPMvPI99SftIDNB/K5atTvul8Yq2lzPv6wUn/Borlq6iZOmzOPOsEqe07tlN7Upn8dvWnazftptDyal8MOcHWtdP/+tu5XNimbP6DwB+2bSDc0oUJq5wfiqfXYxlP27l4N/JpKQ65q/dSLv6mTMVQsXCuYu4vHVzzIzzjnHerlq+hkubNQKgZZvmLJiz6IS3X7VsNSVLnUmJs4pnTaOCZOGcxVzuXcPOq1aVfUn7TqAvW7Dga9/Ha5GiRah8fiUicmf+GL2wZjUKFAztayB418G2LTEzLrjwPJKS9pGYkJiujO86uIqmzRsDcGXblsydPT/TvmbN+JLLW12W9vyiWtUp6AWIQl3tymfx25ZdR66Bs7+ndb3017HK58QyZ5X/NbAQcUXyU/mcGJb9sCX9NbBB6F4Dj2XB1wtp2aaF73O5WlWSkvYH/FxeuWw1jf0/l2cf+VyePP4jGjdrSJGihbOy6tnewm82snPvwWBXQ+SEgkLJwAjg8YwrMmb6mNk+7//GZjbXzCaZ2S9m9pKZdfCyWL41s3J+u2lmZvO9cq297SPM7GUzW+5lxnT02+/XZvY+8C3QFPjLOTcawDmX4tXzLjPLB3wOxHkZLkf9SdjM1ptZjJdt8pOZjTSz78xsnJk1M7OFXjZLHa98fjN7x6vfajNr5+2qBfCNc26tV58dzrkUMzsTKOicW+ycc8C7BA6qHA4Q5vcCXgWBrd66qsBX3uOvgXaZNwfn3O/AE8CjXl3rmNkir56LzKySt3y+mVX364OF5styutSOZFGtNrMCGfafjC94Ut7M7vX6YK2Zfej1OWZ2ndd/a81snhek6Qvc4O33hgD1/hrfcXaft485ZlbLexxjZuu9xwGPDU+C10eZMqW8TKUl3jYfmVkR79itBYwzszXAPmCPmZX3NisJfIgvGIT3/yJLn7X1lZmd7b3GGDN71cy+BvofrS/N7Em/+vchB9gZv5OY4sXSnheLK8aOhF1BrFH2lRifSFyJ2LTnscVjSIxP/+Vp7+69RBfIT4QXQPOVSTzh7UNZQnwicSXi0p7HFY8lIT4hXZk9u/dQoEA0ub0/GuOKx5Gw/cgfTN+u/Z5brr2LTg88ye/r/sj0Gl/M/IoWfn8khaqzYgqwOT4p7fmWhL2ULJb+D+pvf9tOuwaVAahV6SzOLl6IkrEF+H59Ag2qlaZowbzkzZOblheXo1Rc6P4hmRC/g9gM511ChvNuz+69RBeITgt8+8oknvD2s2fNpWnLJumWfTRhKndd35H+vV8haW8SoSAhPpFYv3PYv58OC9yX4XOdO574+ASKZ7gOxmfqw/TXweIlYjP1818H/2LJwqU08QJH4easmIJsjt+b9nxL4l5Kxga4Bjb0roGVz+Ls4oUpGVOA7/9IoEG1s/2ugeVD+hp4LAnxicQV9z8eT+ScPnI8JmxPYN7s+Vx1Xdusq7SI/CsnOgRoCNDBzAr9i31fCDwGXADcClR0ztUBRgKP+JUrA1wKXAkMM99wq7uBPc652kBt4F4zO9crXwfo4ZyrCpwHrPR/UefcXmAjUB5oy5GsoMw/nwRWHl+2TjWgMnAz0ADoAjztlekBzPbq1wR42czy48tUcuYbCrXKzLp65UsC/mNtNnvL0nHOHQIewBfw2oovEDTKW70WuMZ7fDVQwMyKZdyHZ5VXd4CfgEbOuRrAs8DhvOuR+LJkMLOKQB7n3DdeOx/ysoIaAunC117g5zKvjlOcc7WdcxcCP+J73/Be53JveVvn3D/esoneezHxBOp9NMc6NsCXcdTZzDKmq7wLdPMyrb4FejnnJgMrgA5evQ7iC3jV84JnvwJLvOe58R0Ty4E3OZK1NQ7wz42vCDRzznUmQF+aWQt8mWJ18GVP1TSzRhkbaWb3mdkKM1sxacyHx+mS08/hMi0LPABSXOauwjJ0lgtQ6HCZE9k+lB2rb46Uybzd4SKVq1Tk41kT+d/kd7j+5mvo2qlHunKHDh1i/pxFNG3R+FRVOdsKdNhkPJcHTlhE4egolgy7hweuqsXadX+SnJLKzxt38MqExUzvfzNTX7yJb36LJzklNYtqHgQBj7sTKWMntP2hQ4dYOHcxjZsfudy3u64N708bw//bu+84uar6/+Ovd0IJLXRQkU4A6V2qFEW/VAUrTQVERUQQRarS7PWr+LMgCLEA4heUDipCkCI9lFBEqqLSpIROkvfvj3s3mSyb7Owu2TM3834+HvvYuefubt47mZ0zc+45n3PymT9m0cUW4UffnU3qhLXxN9zW/d3N+rp/aKMf6fU1fxl3NWutu+bUpWPdps/nwF7327fPuJqFFhjFX0/aj/132ZBb7/0Pkyabex5+gu+ceQ0XfGsPzvvG7tx236Oz93PgTLTzGnBmfff3v/X/2P/gTzByZHfPJI8OIXX+RwFtLXGx/aykX1DNPml3jtsNtv8NIOk+qlk7UL0hb71UdpbtKcC9ku6nGhR4J7CWps1CWpDqjfQrwPW2ey79Cvp4pppxezsesH17nXsCcJltS7qdagCLOt/Okj5fH48ClqG6PzenGqx4AbhM0k3As7zWa/JJmpNqUGhd4H7gROAI4MtUAww9NXKuBB6hmsXVl9ZH04LAWElj6n+zp+Lbb4EvSjoU2Ac4rW6/GviupF9TDfr8s35SX7GeTWPgXNsX1zNhvgwsRLVs79KWn3GapLOolve1q52/ghk9Nv4GYPsBSddTDeZVP7QazFzI9ri6aWz9+/flaqoZQSOBa4HrqQa01gXusf2SpE2AXeuv/yXwzZbv/209Y63nZ/W+L99Z/w631F8zf53/ytYQtk+imjnFnU+PH+xj+XWz6BKL8sSj067iPvnYkyyy2MIFE3WW3//mPC4852IAVll9ZR77z7SZLY8/+gSLLj59HYIFF16Q5yY+z+RJkxk5x8j6a6ox3sWXXKzf75/d/N+Zv+Pcs6taGG9ZfRUe+89jU8899ujjLLb49KuXF1p4QSZOfI5JkyYxxxxz8Nijj7HYEtXXzDf/fFO/btMtNuabX/keTz/1NAstvBBQ1UZY5S1jWHTR2fs+BXjk8Ym8eYlpV8WXWnw0/3ryuem+ZuILr/CJb18w9fjuXx3Ag/95GoCxl9zK2EtuBeC4fbbikSdmj5ksPX73m/O44Jyq5sWqq6/C473+7hZbfPrrLtXf7XNMmjSZOeq/28Va/m5n9v3XXXUDK6+6EossOu15s/X2DrtuxxGf+eLr+wsOo9+deW6v+3La3/BA78tu9dszzuH3Z58PwGprrMqjvZ4HF19i+vtnoYUXmu558NH/PM5ivb7mDxdfxju3e8esD9+hHnn82elm9yy12Gj+9UQfz4HfPH/q8d2nH8iD/6lmQo+9eDxjLx4PwHH7bs0jj/f1cn72dPaZv5taq+8tq6/KY4+2Ph6f6LNfnv5v+vGpf9N3T7iHYw47HoBnnnqGa/9yHSNHjuRt22w+TL9NRPRnIMWC/5dqlsZ8LW2Ten5GvdyptZbLyy23p7QcT2H6wajeb3hNNThwYEvdn+Vt9wwqPd/ytROolv9MJWk0sDRwX3u/1mu0k1vAe1vyLWP7LqoZQONsP2H7BeAiYL26/c0tP/fNwL/qpVA9y4uOp5o5gu376mVmZ1EvXbL9L9u71jN+jqrbnpnB77Au1cwdgBOAy22vAexENYBFne+PVMvQPgCcXrd/HfgYMA/wV0k9M3d6Zlyta/vYuu004NO21wSOa/nZnwSOpvp/GD+TGU0zyz31sdXzc2sze2z0+CpwGAN7fPe4huo+3xS41vbE+t/fihnXE2p9DE99fM7gvhTwtZb8K9k+hQ435i0r8u9//IdH//UYr746iav+eA0bvm2D/r+xS7zngzvzs9/8mJ/95sdsvvWm/PGCP2GbO2+7i/nmn3fqgE8PSayzwdqM+1M1gfEP5/+RzbaqylltuuXG/X7/7OZ9H9plahHoLbfZgovOvxTb3HHrBOZfYL7XvFmUxPobrsPlf6zGeS8671K22Koq3vvkE09OvWI54fa78JQpLLjQtEmu1Zuk2X/pGMCN9/yLlZZahGXfsCBzzjGC92+1Ghde87fpvmbB+eZmzjmqp8q9t1+Hq25/mIkvvALA4gvNC8DSS4zm3Zuvwll/njC8v8AstssHd55a5HnzrTfl0gv+iG0m3HYX880/X59/t+tusDbj/lSN4V8y3d/tJjP9/ssuuZy391o61lpn56o/X83yKy43i37TWW+XD72bU876Kaec9VM233ozLq2fwybcdmeb9+Uf2GyrQe81MVt4/267Ti0OveU2W3DReZdgm9tvncD888//mjfh1fPguvz5j1cAcOF5l7Dl1tMqJTw38TluuXE8W27dvW+8b7y75zlwoeo5cJvVufDamTwH7rAuV902g+fALVad7Z4DZ+a9H9qFsWedzNizTuZtW2/GJef/oeqXb7uT+efvu19eb8N1uaK1X66L6v/fxWdw9sVncvbFZ7LVtlvy+aMOzoBQRIdpuxiu7f/WMz/2BX5eNz8IrE81ePFups1CGYj3SxoLLA+sANxDNeNkPGSXpAAATb5JREFUf0l/tv1qvbzpkT6+9zLg65I+bPsX9ZKh7wCn2X5hFi65uBQ4UNKB9SyidW3fUrd/oV5i9QrVsrjv2f63pImSNgauAz4MnFjPKFmn54eq2gVrNUmL234c2JZ6kERVoe//1rOqjmDa/8F0JC1HVZS6Z8uiBZl2332015efDJwP/MX2f+vvX7GeKXV7PSNmVWD8DO6HBYB/1zOc9uj5d+qfcR1wnaSdqAaHJtZf3ydJW1LVE+p51fwg1WPreqB1C5J+Hxu275Z0J7Aj1cyyZyQ9JWmLehnhXkDPrKHeue6kKjy9BfCpum088EmgZzngNVQFvX9Z/959VridwX15KXCCpF/bfk7SUsCrth/r62d0ipFzjGS/z+/DcZ/5KlOmTOHtO23FMisszSXnVFve/s+u2/LUk09z6EeO4IXnX0QjxAVnXsQPzvwO884/b+H0w+utm2/EdVfdwJ47782oUXPzhWM/N/Xc4Z8+ms9/6bMstsSifPygfTnh8K/y8x+dxkqrrMR273lXv99/wuFf49abbuOZp5/hA+/ag49+ci+23+U1NesbbdMtNuaav/yV9+2wO6NGzc3RJxw+9dxnP/UFjjz2Cyy+xGIc8NlP8sUvHMdPf3gKK6+6EjvvugMAf/7jOM4561xGjhzJ3HPPzQnfPGbqFPaXXnyJ66+9kcO/+Lk+/+3ZzeQp5rMnXsr5X9+NkSNGMPaSW7nroSf42I7rAXDyBTez6jKLcfJhOzN5yhTufugJPvmdabu4nXHMe1lk9Dy8OmkKB594KU8/91KpX2WW23jzjbjuquvZY+ePMveouTns2M9PPXfYp4/i0C8dwmJLLMonDvoYxx/+VU750VjGrLIi27/nf/r9/pdefImbrruZzx198HT/5k++fzJ/v+c+JPGGNy7J544+aFh+11lt4y024rqrrmOPnT5S3RfHtdyXBxzJocccwmJLLMYnDt6P4w/7Cqf8v9Oq+7J+Lnvyif/yid0P4IXnX0AS//frcxh7zsnMN/98HH/4Vxh/Y/Uc+L537sbe+3+YHXbZrtSvOststsUmXHPlX9l1+w8xatQovvjlI6aeO3j/QznquMNYfInFOPCz+3PUF47lJyeezMqrjpn6PAhwxWVX8tZNN2SeeeeZ7mcf/YVjuemGW3j66WfY8e27st8B+/DuXXcctt9tOFXPgZdw/jd2Z+RIMfbiW7nrwcf52E71c+D5N7Pqsotx8uHvZvIUV8+B35o2a+iMY99fPQdOnsLB3794tn4OnJlNttiYa6+6jg/suCejRs3NkccfNvXc5w44nMOP+TyLL7EY+x/8cY75wgmc9P9OYeVVx7DjLr33l4nexh69K1ussyyLLTgvfz/rYE447QrGXjS+dKzZWu9ltlFRX2tAp/sC6Tnb89e3lwQeAL5p+9j6+FyqGRmXUc3gmF/SVsDnbfcUjr6iPr6x9Zyk04CnqGb7LAkcYvsCVdudf5lqZouoCgi/h2omydSfW//spYEfUb3hHkE1O+fztl+uB0guqGfJ9Hx9X20P1hnmbz1X57vA9v+1fp+keahmTm1a53uw5Xfdk2rQxsBFtr9Qt29ANbNmHuDi+r7qawnZJ6lqMb0KPAR81PaT9XKpr9U/90qqWjU9v+NdVLWDRlENcvzYdfHtejBibH0f/hnYy/ZyLf/e3cDBti+pj0+kGpiZTDVA8lHgjb3vs/pr96caKHmIalngArY/KukcqiVRonpcHAwsTDUgMmf9e8wDfItqQGdeqsfV8bavrn/2qlSDjc/Vufe0vdxMHhsL9/q/W5tqidY+tk9TVVT7J/W/dT+wt+2nJL2XambRi8Amtl+UdCGwoO3N65/1UeBU4E31AN9yVINyi9X//t62H259vMzovqz/zw6imkFE/fvtaXuGM9s6YflYky0410KlIzTavCNH9f9FMVNv2qHjJwN2tPvP27N0hGZLsZ4hy/Pg0Lzhf35aOkLj/eOiffv/opihpbc7uXSExnvx8i/NFp3J06881vHvqxaaa4lhv6/7HRSK2Vc9M+kKYNV6BlJ0oAwKDU0GhYYmb4aGLoNCQ5NBoSHKoNCQ5XlwaDIoNHQZFBqaDAoNXQaFhk+JQaHB1FyJ2YCkD1MtZTsqA0IRERERERExO1MDPkpou6ZQzF5s/4Jqm/aIiIiIiIiI6EKZKRQRERERERER0YUyUygiIiIiIiIiZmvZfaxvmSkUEREREREREdGFMigUEREREREREdGFMigUEREREREREdGFUlMoIiIiIiIiImZvSk2hvmSmUEREREREREREF8qgUEREREREREREF8rysYiIiIiIiIiYrWXxWN8yUygiIiIiIiIiogtlUCgiIiIiIiIiogtl+VhEREREREREzNaUBWR9ykyhiIiIiIiIiIgulEGhiIiIiIiIiIgulOVjERERERERETF7U5aP9SUzhSIiIiIiIiIiulAGhSIiIiIiIiIiulCWj0VERERERETEbC2Lx/qWmUIREREREREREV0og0IREREREREREV0og0IREREREREREV0oNYUiIiIiIiIiYramVBXqU2YKRURERERERER0oQwKRURERERERER0oSwfi4iIiIiIiIjZWpaP9S0zhSIiIiIiIiIiulAGhSIiIiIiIiIiulCWj0VERERERETE7C2rx/qUmUIREREREREREV0og0IREREREREREV0oy8ciIiIiIiIiYraW3cf6lplCERERERERERFdKINCERERERERERFdSLZLZ4iIBpP0cdsnlc7RZLkPhyb339Dk/hu63IdDk/tvaHL/DV3uw6HJ/Td0uQ+jpMwUioih+njpALOB3IdDk/tvaHL/DV3uw6HJ/Tc0uf+GLvfh0OT+G7rch1FMBoUiIiIiIiIiIrpQBoUiIiIiIiIiIrpQBoUiYqiy/nnoch8OTe6/ocn9N3S5D4cm99/Q5P4butyHQ5P7b+hyH0YxKTQdEREREREREdGFMlMoIiIiIiIiIqILZVAoIiIiIiIiIqILZVAoIiIiIiIiIqILZVAoIgZM0nySRtS3V5a0s6Q5S+dqktyHrw9J85XOEBFRkqSFJa1VOkdERDRTBoUiYjCuBEZJWgq4DNgbOK1ooubJfTgEkjaVdCdwV328tqQfFY7VKJI2l7R3fXtxScuXztQkkt4s6XeSHpf0qKSzJb25dK6mqAfDL5N0R328lqSjS+dqCklXSBotaRHgVuBUSd8tnatJJC0p6RRJF9fHq0nat3SuppC0Wc+FGUl7SvqupGVL52qa9MXRCTIoFBGDIdsvALsCJ9reBVitcKamyX04NN8D3gU8CWD7VuBtRRM1iKRjgMOAI+qmOYFflUvUSKcC5wFvBJYCzq/boj0/o3r8vQpg+zbgQ0UTNcuCtp+l6kNOtb0+8I7CmZrmNOBS4E318d+Ag0uFaaAfAy9IWhv4AvAQ8IuykZolfXF0igwKRcRgSNImwB7AhXXbHAXzNFHuwyGy/Y9eTZOLBGmmXYCdgecBbP8LWKBoouZZ3PaptifVH6cBi5cO1SDz2r6+V9ukIkmaaQ5JbwQ+AFxQOkxDLWb7LGAKgO1JpB8ZiEm2Dbwb+L7t75N+ZKDSF0dHyKBQRAzGQVRXNX5ne4KkFYDLC2dqmoPJfTgU/5C0KWBJc0n6PPVSsmjLK/WLeUNqMw3SE/WSiZH1x57UM9eiLU9IWpFpj8H3Af8uG6lRjqea5fJ32zfUfci9hTM1zfOSFmXaY3Bj4JmykRploqQjgL2ACyWNpJrpEu1LXxwdQdXjMCKiPXWn/3Xbh5bOMjuQNJ/t50vnaBpJiwHfp1ouIeAPwEG286a8DfUg2hhgW+BrwD7A6bZPLBqsQSQtA/wQ2ITqBf01VI/Bh4oGa4h6EOMkYFPgKeABYE/bD5bMFd1D0nrAicAawB1UM/3eXy9Hjn5IegOwO3CD7b/Uz4lb2c4SsjalL45OkUGhiBgwSX+2vU3pHE1WLx07BZjf9jL1mvxP2P5U4WjRJSRtC7yTalDtUtt/LBwpulB9ZXyE7YmlszSJpMWB/YDlaFl6bHufUpmaRtLcVMvFVqF6HryH6rH4ctFgDVIXlh5j+0+S5gVG5m95YNIXRyfIoFBEDJik71Bd2fgt9TpoANvnFAvVMJKuA94HnGd73brtDttrlE3WDJJ+0EfzM8CNts8d7jxNU78Rf8n2ZEmrUL0putj2q4WjdTxJX7D9TUknUk/5b2X7MwViNY6khYAP89pBjdx/bZB0DfAX4CZa6uDYPrtYqIaRdLPt9fpri75J2g/4OLCI7RUljQF+YvvthaM1Rvri6BQpahoRg7EIVe2M1tlCBjIoNAC2/yGptSkFLts3CliVamAS4L3ABGBfSVvbPrhUsIa4EthC0sLAn4AbgQ9SFT6PmeupXXVj0RTNdxHwV+B26kK/MSDz2j6sdIgmqpc9LQXMI2ldqhkaAKOBeYsFa54DgI2A6wBs3ytpibKRGid9cXSEDApFxGB8zvZ/S4douOkKJQOfIYWSB2IlYJt6txgk/ZiqrtC2VG8yY+Zk+wVJ+wIn1jNfbikdqglsn1/ffMH2b1vPSXp/gUhNNcr2IaVDNNgFkra3fVHpIA30LuCjwJuB77a0TwSOLBGooV62/UrPxS1Jc9DH7MmYqfTF0RGy+1hEDMZ1kn4raXv1muoSbfsk1VW2pYB/AuvUx9GepYDWXTrmA95kezKQehD9U13Xag/gwrotF4oG5og226Jvv5S0n6Q3Slqk56N0qAY5iGpg6EVJz0qaKOnZ0qGawPZY21sDH7W9dcvHzlkGPyDjJB1JNeNqW6qZu+f38z0xvfTF0RHyoIuIwViZatenfYATJf0GOM3238rGag7bT5DpwUPxTWC8pCuopv6/DfhqvT7/TyWDNcRBVAMYv7M9od4J6vLCmRpB0nbA9sBSvWpbjQYmlUnVSK8A3wKOYtrsAgMrFEvUILYXKJ2h6WyfLWkHYHWqJck97ceXS9UohwP7Us3O/QTVktCTiyZqnvTF0RFSaDoihkTS1sCvqGZq3Aocbvvasqk6X3aOGTpJbwL2Au6mevz90/aVZVN1Pkkjga/bPrR0liaqdwpcBzge+FLLqYnA5bafKpGraSTdB7y1HiCPQajrkIxh+gGNPAe2SdJPqGoIbU01mPE+4Hrb+xYNFl0hfXF0kswUiogBk7QosCfVG/JHgQOB86jeKP0WWL5YuOY4l2rnmD+RAtMDJuljVFfY3gyMBzYGrmX64ufRh3qXk/VL52gq27cCt0o6PTvEDMkE4IXSIZoqz4Gvi01tryXpNtvH1TurZvlYmyRtBhwLLEv1nlKAbWe2XxvSF0cnyaBQRAzGtcAvgffY/mdL+431lbfoX3aOGZqDgA2Bv9reWtKqwHGFMzXJLZLOoxrEfb6nMfU0BmQ5SV8DVmP6mRp5Q9SeyVRLQC+npQ5YtqRvW54Dh+7F+vML9czTJ8lFrYE4BfgscBO5uDVY6YujI2RQKCIGYxXPYO2p7W8Md5iGys4xQ/OS7ZckIWlu23dLWqV0qAZZhOoNUOusApOr5ANxKnAM8D2q5Sd7M21r6+jf7+uPGJw8Bw7dBZIWoqptdTPVc+DPiiZqlmdsX1w6RMOlL46OkJpCEdG2ev3zx6imq19s+5qWc0fb/nKxcA0jaSJVHZyXgVeZNu16dNFgDSHpd1Rvwg+mejH1FDCn7e1L5oruIekm2+tLut32mnXbX2xvUTpbU0iai2rjAoB7shyvfXkOfH1Jmptqxt+qtq8rnacJJH0dGEk1gNE62+/mYqEiYlAyKBQRbZN0MlVRxuup6gmNs31Ife5m2+uVzBfdSdKWwILAJbZfKZ2nCSS9GTgR2IzqquRVwEG9loPGTEi6GtgC+D/gz8AjVEVDM1ujDZK2AsYCD1INii8NfCSFkgcuz4GvH0kP216mdI4mqJd+9mbbqWvVpvTF0SkyKBQRbauLMa5V354D+BGwGLAbVV2DdUvmawJJq9bT/PscQMsVthgOkv4InE5VGwyqwvF72N62XKpmkbQhcBewEHAC1Zvyb2SWQXsk3QTsbvue+nhl4AzbKbzapnonvJ6ZaX+pi6DHEEj6h+2lS+eI7pC+ODpFBoUiom2S7ra9aq+2LwHvApawPaZMsuaQdJLtj+cKW5Qkabztdfpri/bVA+UftP3r0lmaoPUiw8zaom+SDgL2Y1rtkV2Ak2yfWC5V82WmUP8k7Wn7V5IO6eu87e8Od6amSl8cnSKFpiNiIG6U9D+2L+lpsH28pH8BPy6YqzFsf7z+vHXvc5I2Hv5E0aWekLQncEZ9vBtVscvoh6TRwAHAUsB5wB/r488DtwIZFGrPjZJOYdoV8j2odjGK9uwLvNX28wCSvkG1M2gGhfoh6XyqpTqvOQUsOsxxmmi++vMCfZzLbIOBSV8cHSEzhSIiOkSuUMZwkbQM8ENgE6oX8ddQ1TF4qGiwBpB0LlVR32uBtwMLA3NR3X/jC0ZrlLqw7wHA5lRvxq8EfmT75Zl+YwAg6XZgQ9sv1cejgBt6ip7HjNU1mGbI9rjhyjK7kXSw7f8tnaMp0hdHp8igUEQMiqRNgeVomXFo+xfFAs0GUssgZrV6QOMqqheeN6Qo7cD12m1sJPAEsIztiWWTNYuk+ai2VZ9cH48E5rb9QtlkzVAv3fkI8Lu66T3AaXlDPjiSFgaWtn1b6SxNlotb7UlfHJ0mg0IRMWCSfgmsCIwHJtfNtv2ZYqFmA3kxFbOapB2BTeuPNYF7gKupXpheY/vRgvEaofdOi9l5cXAk/RV4h+3n6uP5gT/Y3rRssuaoNyxonWn1qO1/lU3VHJKuAHamurg1Hnicll1VY+Bycas96Yuj02RQKCIGTNJdwGrOE8iA9VPLYBvb8/VxLuJ1V8/MWBfYCvgksLztkUVDNYCkycDzVH+zAPMAL9THtj26VLYmSYHV118uLAyMpFtsryvpY1SzhI5JsfOhyWNw4NIXRydIoemIGIw7gDcA/y4dpIG+PchzEa8LSYsx7QrlxsAo4E9UNXKiH3mx/rp5XtJ6tm8GkLQ+8GLhTE2n/r8kWswh6Y3AB4CjSodpCkkTqS5uiekvcolqkDzakL44OkkGhSJiMBYD7pR0PTC1KKjtnctFaoYUsIySJN0LPAOcDVwKfLln+U4MjKRf2t6rv7aYoYOB39a7VwK8EfhguTizhczeHZjjqZ4Hr7J9g6QVgHsLZ+p4tvvadSwGIH1xdJosH4uIAZvRzh0Z8OhfvWPMDJ94M209ZiVJR1BdkVwK+BvVFclrgVt6Cv5Ge/qoLTQHcJvt1QrGahRJcwKrUM0wuNv2q4UjdTxJJzLjJcgfyfLF9klaxPZ/S+doIkkjqJ7v1iidpYnSF0enyaBQRMQwkrRsffOA+vMv6897AC/YPn74U0U3krQy1bT1TYAtgMdtz3Sr5pj6Yv5Ipq8lBPAKcJLtI0pla5rsYjlwkj4ys/O2xw5XlqarZ2uMB04FLk6dxIGR9GvgCNsPl87SZOmLoxNkUCgi2ibpKtubt6wnn3qKFFgdEElX296sv7aIWaFeJrEpsFn9+U3AdbZ3LBqsQSR9LQNAg5ddLKM0SQLeAewDbAT8BjjN9t+KBmsISX8GNgSupyq+D6SUwECkL45OkUGhiIgCJI0HPm37qvp4U+BH2XknZiVJv6Oasv4s1da3V1Ntf3tn0WANVL+h3IVqS3ADf7H9+6KhGiS7WA7OTHawBPKGfLAkbQ38CpgPuBU43HYK/s5ESgkMXvri6DQpNB0RAyZpkZmdzxr9tuwL/FzSglQv8J+huloZMSudCuwHPGf7pdYTkua2/XLf3xZ9+H/ASsAZ9fEnJW1r+4CZfE9Mk10sB6dnl8pdqe6/X9XHuwEPlgjUVJIWBfYE9gIeBQ4EzgPWAX4LLF8sXDM8DKxO9RrmLtv3F87TJOmLo6NkplBEDJikB4Glgaeolo4tRPXiAKrp/yuUSdY8kkZTPRc/UzpLdI/eRZJn1BYzJmkCsEbPTJe68Orttlcvm6wZJF1O9eY7u1gOgqQrbb+tv7aYMUl/o6rrd6rtf/Y6d5jtb5RJ1tnq1y0nA+tTzaoSsDZwE7Cv7WcLxmuU9MXRKTJTKCIG4xLgPNsXAUjaDniH7c+VjdUckpYEvgq8yfZ2klYDNrF9SuFoMRuT9Aaq3U7mkbQu04okjwbmLRasme4BlgEeqo+XBm4rF6dxji0doOEWl7RCz+wMScsDixfO1DSrzGj5YgaEZuoHwJ3Ah2xPganLab8I/BD4cMFsjZC+ODpNZgpFxIBJusn2+r3abrS9QalMTSPpYqrpw0fZXrvezvoW22sWjhazsXrnoo8CGwA3tpyaSFVg9ZwSuZqkpabLgkwrsmrgrVQ1Id5RMF50CUn/A5wE9CzZWQ74hO1Li4VqGEmLA1+gWgI1qqfd9jbFQjWApHttjxnouZgmfXF0mswUiojBeELS0VS1DEy1Jv/JspEaZzHbZ9XbW2N7kqTJ/X1TxFDU21WPlfRe22eXztNQ3+7/S2JGWnavFNnFctBsXyJpDLBq3XR36pAM2K+pdhzbEfgk8BHg8aKJmkH9f0nMTPri6DQZFIqIwdgNOAb4XX18Zd0W7Xu+LnLZU49kY6pi0xHD4TJJ3wV66o+MA45Pbav+te6sI2lZYIztP0mah7yu6pftBUpnmI2sTzVDaA5gbUnY/kXZSI2yqO1TJB1U/12Pk5Sds/p3taQvASe0Lr+T9EXgr+ViNVL64ugIefESEQNW7y52UOkcDXcI1S4nK0q6mqoWxPvKRooucgrV7k8fqI/3olrOuGuxRA0jaT/g48AiwIrAm4GfAG8vmaspJH0b+Hm2YB4cSb+ketyNB3pmmRrIoFD7Xq0//1vSDsC/qP6OY+YOpOpD/i5pPNXjbj3gZqqdVaN96YujI6SmUES0raWWRp+ya0x7JI0EPgOcCKxCNRX7HtuvzvQbI14nksbbXqe/tpix+s3QRsB1ttet225PXbD2SPoYsDfVBcpTgTNydbx9ku4CVptRoeTon6Qdgb9QFYk/karI73G2zysarCEkrQisRvUaZoLt+wpHapz0xdEpMlMoIgYitTReB7YnS3q37e8BE0rnia70oqTNbV8FIGkz4MXCmZrmZduvVJvuQF0sPm/Q22T7ZOBkSatQDQ7dVs+a/Jnty8uma4Q7gDcA/y4dpKlsX1DffAbYumSWhnoD8Gfbz0vaU9IBwPdtP9TfN8ZU6YujI2RQKCLa1quWxuJ1W4oyDs7Vkn5IVeTy+Z5G2zeXixRdZH+qIpcLUl3l/S/VTijRvnGSjqTaUnhb4FPA+YUzNUo9a3LV+uMJ4FbgEEmfsP2houE632LAnZKuB6YWmM6M3fZI2hr4NNMKdd8F/ND2FcVCNc+PqWpZrU21i9spVMsXtyyaqlnSF0dHyPKxiBgQScdQrScXMAKYBJxo+/iiwRpGUl9Xwp2tcGM4SRoNYPvZ0lmaRtIIqvoZ76R6PrwUODnLedpTF1fdGbgMOMX29S3n7rG9SrFwDSCpzzferRdvom91/aAfAsdT1cERVU2co4FP276oYLzGkHSz7fXqotOP1EW7b7a9XulsTZO+OErLoFBEtE3SZ4HtgY/bfqBuW4HqatEl9XKoiOhQkj48s/PZuWhgMmNy8CTtA5xp+4U+zi2Y+kIDUy872d32AaWzdDpJVwAH2b61V/taVBe5MtOlDfVObZdQLf98G/A4MD511fqXvjg6TQaFIqJtkm4BtrX9RK/2xYE/9BRbjf7VV9ZeIzOuYlaSdGJfzcBOwFK2s6y8H6qKCB1DtfRE9cdkMmOyLZJmOosgS2jbJ2kdYHeqnYseAM62/cOioRpA0t22Vx3ouZiepDdQPf5usP0XScsAW2VAo3/pi6PT5AEXEQMxZ+8BIaiukkuas0SgBnu+5fYoYEeqmgYRs4ztA3tu14MbewCHAX8FvlIqV8McDGwGbNh7xqSkz2bGZL++M5NzBrKEdiYkrQx8CNgNeJKqLp1sp1By+54f5LloYfs/wHdbjh+mqikU/UhfHJ0mM4Uiom0zWyuedeRDI2lu4Dzb7yqdJWZv9S5ZHwU+B1wHfM32PUVDNUhmTEZJkqZQbaO+r+2/1233216hbLLmkPQ0cGVfp4DNbS88vImaSdKuwDeAJZg2a9K2RxcN1hDpi6OTZKZQRAzE2pL6KoInqtkuMXjzAnlRH7NUvWXwQVTFff8nWwcPSmZMvk4kbQosR8vr0Sw96dd7qWYKXS7pEuBMqj442vfumZz79rClaL5vAjvZziznAUpfHJ0mM4UiIgqQdDvVUgmAkcDiwPGpBxGzUj3L4DGqgqCtLwB6rvCuVSRYg2TG5OtD0i+BFYHxVDWZoHoMfqZYqAaRNB/wHqplZNsAY4Hf2f5DyVzRPSRdbXuz0jmaKH1xdJoMCkXEgElaEfin7ZclbQWsBfzC9tMlczWJpGVbDicBj9qeVCpPdIdej7vXyNXK/kmaTN91RwSMsp3ZQm2QdBewmvNCdMgkLQK8D/iQ7dRk6oekBYEjqAbVFq+bHwPOBb6e1zLtkfR94A3A74GXe9ptn1MqU1OkL45OM6J0gIhopLOByZJWAk4BlgdOLxupcd4I/Nf2Q7YfAUZJemvpUDF7qx9vD7W84BxT334M+G/BaI1he6Tt0X18LJABoQG5g+oNZQyCpBXrWnRQXZgZBexaMFKTnAU8RbVT1qK2FwW2Bp4GflsyWMOMBl4A3km1a9ZOVJtmRD/SF0enyUyhiBiwniUSkg4FXrJ9oqRbUmC1fXWx2vV6rpJLGgHcmKUnMRwk7Qd8HFjE9oqSxgA/sf32wtFiNifpfKrlEgsA6wDXM/0sg53LJGsWSeOBDahqMl0KnAesYnv7grEaQdI9tlcZ6LmI11v64ugUKTQdEYPxqqTdgI9QXRkCyBXygVHrsgnbU+qdKCKGwwHARlQ7nmD7XklLlI0UXSKFfF8fU2xPkrQL8L89F2dKh2qIhyR9ARhr+1EASUtS7QT1j5LBmkTSKGBfYHVaNhuxvU+xUM2Tvjg6QpaPRcRg7A1sAnzF9gOSlgd+VThT09wv6TOS5qw/DgLuLx0qusbLtl/pOagHJDN1OGY52+NsjwO277nd2lY6X4O0Xpy5oG7LxZn2fBBYFBgn6SlJTwFXAIsAHygZrGF+SbUE9F3AOODNwMSiiZonfXF0hCwfi4gooL4S9AOqXWNMtS3pwbYfKxosuoKkb1LVz/gwcCDwKeBO20eVzBXdo6+d2iTdll132iNpNeCTwLW2z6gvznzQ9tcLR4su0VM2oOfvVtKcwKUpdt6+9MXRKTIoFBEDJukB+riSYXuFAnEiYoDqGlb7UhUIFVVNkpOzE1TMapL2p3rjswJwX8upBYBrbO9RJFgDSZoHWMb2PaWzNE29i+ouwNJUO4DeC5xh+5miwRpE0vW2N5J0JdXf9H+A6/NasH3pi6NTZFAoIgZM0qIth6OA91MVyftSoUiNI2ll4MfAkrbXkLQWsLPtLxeOFhExy9TbgS8MfA04vOXURNvZdadNknaiqs80l+3lJa0DHJ9C3f2T9Bmqeog9SxbHU+1GtgvwKdtXFAvXIJI+RrUb7VrAqcD8wBdt/7RosIgYsAwKRcTrQtJVtjcvnaMpJI0DDgV+2rNrm6Q7bK9RNll0g8z2i04gaSSwJC0bn9h+uFyi5pB0E9Xy4yta+pDbba9ZNlnnk3Q7sI7tyZLmBS6yvZWkZYBzs5NqDJf0xdEpstNNRAyYpNY6ECOotsVdoFCcpprX9vWSWtsmlQoTXWeDlttTZ/sVyhJdSNKngWOBR4EpdbOpZh1E/ybZfqZXH5Irve2bA5gMzE39+sX2w3VdnGhDPevvWGCLuukK4IQswRuQ9MXRETIoFBGD8Z2W25OAB8mOHQP1RF3TwACS3gf8u2yk6Ba2n+zV9L+SrgKyBDSGy8HAKn08FqM9d0jaHRgpaQzwGeCawpma4mTgBkl/Bd4GfANA0uJAljC27+fAHUx7/bcX1TKyXYslapj0xdEpsnwsIqIASSsAJwGbUtUyeADY0/aDJXNFd5jBbL/9ba9dKFJ0GUmXA9vazgzJQaiXPR3F9AVqT7D9UtFgDSFpdeAtwB227y6dp4kkjbe9Tn9tMWPpi6NTZFAoIgasnjJ8DNUVNqiKNR6fKcMDJ2k+YITtiaWzRPeo35D36Jnt9+3sYhTDRdIpwCrAhcDLPe22v1ssVHSVerbuP22/LGkrqqWLv7D9dMlcTSHpWuBQ21fVx5tR9SOblE3WHOmLo1NkUCgiBkzS2VRThsfWTXsBa9vOlOE2STqIapr1ROBnwHrA4bb/UDRYRMQwkHRMX+22jxvuLE1U72D5eWA5pi/UvU2pTE0jaTzVzIzlqGZanUe1pHH7grEaQ9LawC+ABeump4CP2L6tXKqIGIwMCkXEgGXK8NBJutX22pLeBRwAfBE41fZ6/XxrxJBIWgX4OLBq3XQXcJLtv5VLFd1K0gKAbT9XOkuTSLoV+AlwE1XBZABs31QsVMNIutn2epIOBV6yfaKkW7L72MBIGg1g+1lJB9v+38KRGiF9cXSSEaUDREQjvShp6vbz9ZThFwvmaaKeLWO2pxoMurWlLWKWkLQJ1Q4xz1HVtPoZ8DxwhaSNC0aLLiNpDUm3UM06nSDpprrOS7Rnku0f277e9k09H6VDNcyrknYDPgJcULdl97EBsv2s7Wfrw0OKhmmI9MXRaTJTKCIGTNI6VEvHFqQayPgv8NF6YCPaIOlUYClgeWBtYCRwhe31iwaL2Zqki4Fv2L6iV/uWVMsXtysSLLqOpGuAo2xfXh9vBXzV9qYlczWFpGOBx4DfMX1Npuye1SZJqwGfBK61fYak5YEP2v564WiNJekftpcunaPTpS+OTpNBoYgYtNYpw6WzNI2kEcA6wP22n5a0KLBU1uLHrCTpb7ZXnsG5e2yvMtyZojv1LKHtry36JumBPppte4VhD9NgkuYBlklh39eHpIdtL1M6R6dLXxydZo7+vyQioiJpT9u/knRIr3Ygu8YMhO0pkh4FVpOU5+IYLjPb5e75YUsRAfdL+iLwy/p4T6CvgY7og+3lS2doOkk7Ad8G5gKWr2dBH29756LBOpykiUDPrIKeZe+ub89TJFTzpC+OjpI3IhExEPPVnxcommI2IOkbwAeBO5lWJNTAlcVCRTdYWtIP+mgX1XLGiOGyD3AccA7V4+9KYO+iiRpA0ja2/yypz90+bZ8z3Jka7FhgI6raLtgeXy8hi5mwndeAQ5e+ODpKBoUiom22fyppJPCs7e+VztNw76Ha+vbl/r4w4nV06EzO3ThsKaLr2X4K+EzpHA20JfBnYKc+zplqkC3aM8n2Mz2znWupqzEA9aYjY2yfKmkxYAHbmfHXv/TF0VEyKBQRA2J7sqSdgQwKDc39VLucZFAoho3tsQCS3m/7t63nJL2/TKroJpLOm9n5LN2ZOdvH1J9fM6tK0nuHP1Gj3SFpd2CkpDFUg5TXFM7UGJKOATYAVgFOpVqG9ytgs5K5miB9cXSaFJqOiAGT9BWqncd+Q8vaZ9s3FwvVMJLOptp17DKm3zkmV85jlpN0s+31+muLeL1Jehz4B3AGcB3TapIAYHtciVyzgxT5HRhJ8wJHAe+kehxeCpxg+6WiwRpC0nhgXeBm2+vWbbfZXqtosAZJXxydIjOFImIwerYMPr6lzcA2BbI01Xn1R8SwkbQdsD2wVK96BqOBSWVSRZd5A7AtsBuwO3AhcIbtCUVTzR7U/5dED9svUA0KHVU6S0O9YtuSDCBpvv6+ISrpi6PTZFAoIgbM9talMzRdz9ThiGH2L6p6BTsDN7W0TwQ+WyRRdBXbk4FLgEskzU01OHSFpONtn1g2XeNl+v8ASNoAOBJYjpb3RJnp0razJP0UWEjSflTF408unKkp0hdHR8nysYgYMElLAl8F3mR7O0mrAZvYPqVwtI4n6Xamf+Fu4AngcuDbmbYew0HSnLZfrW8vDCxt+7bCsaJL1INBO1ANCC1HNWvy57YfKZmrCfroQ6aeAla2PfcwR2osSfdQFfy9HZjS0277oWKhGkbStrQsv7P9x8KRGiV9cXSKDApFxIBJupiqqOBRtteWNAdwi+01C0freJKW7aN5EeAjwHy29xvmSNGFJF1BdYVyDmA88DgwzvYhBWNFF5A0FlgDuBg40/YdhSM1ygz6kKkyoNE+SVfZ3rx0jqaS9A3bh/XXFjOWvjg6RQaFImLAJN1ge0NJt7QUFxxve53C0Rqt9f6MmJV6HmuSPkZ1ZfKYFAiN4SBpCtM2KGh9ESrAtkcPf6rmyRvyoZP0dqrZar03fDinWKgGmUGR5PQjA5C+ODpFagpFxGA8L2lR6hf0kjYGnikbabYwonSA6BpzSHoj8AFSZDWGke08z70+tgV6DwBt10dbzNjewKrAnExbPmYgg0IzIWl/4FPACpJalzotAFxdJlVjpS+OjpBBoYgYjEOoakCsKOlqYHHgfWUjNYOkvrYZXRjYE7hymONE9zqeavvlq2zfIGkF4N7CmSKiH3lD/rpaO8veB+V0quWfXwMOb2mfaPu/ZSI1Vvri6AhZPhYRbZO0IfAP2/+p6wh9AngvcCfwpbwY6J+ky3s1GXgSuAI4qafgYERERG+SFqS6kJA35EMk6WfA92zfWTpLk0laAhjVc2z74YJxImIQMigUEW2TdDPwDtv/lfQ24EzgQGAd4C22M1uoH5IOsv19SZvbvqp0nuhOkkYB+wKrM/2L+X2KhYqIfkkabftZSYv0dT4DQ+2TdBewIvAAVU2hnrpWqefSBkk7Ad8F3gQ8BiwL3GV79aLBGiR9cXSKrOuOiIEY2fKC84NUM1vOtv1FYKWCuZpk7/rzD4qmiG73S+ANwLuAccCbgYlFE0VEO06vP98E3Fh/vqnlONr3P8AYqi3VdwJ2rD9He74MbAz8zfbywNvJEsaBSl8cHSE1hSJiIEZKmsP2JKrO/+Mt5/J80p67JD0ILN6rHkSuUMZwWsn2+yW92/ZYSadT1TWIiA5me8f68/KlszRVz2wr8uZ7qF61/aSkEZJG2L5c0jdKh2qY9MXREfImLiIG4gxgnKQngBeBvwBIWonsPtYW27tJegNVp79z6TzRtXpqVz0taQ3gP8By5eJERDtmsFnBVLZvHq4sDXY61aygm6jq+qnlnIEVSoRqoKclzU+1ScavJT0GTCqcqWnSF0dHSE2hiBiQevv5NwJ/sP183bYyMH9ejLavXke+EtUL0Ptsv1Q4UnQRSR8DzgbWAk4F5ge+aPunRYNFxEy1bFYwCtgAuJVqUGMt4Drbm5fK1hSS3mv77NI5mk7SfFQXCEcAewALAr9KXav2pS+OTpFBoYiIYVTv2vZVqtpCD1O9mHoz1YuBo7L7WMxqkt5DNSB5u+1MU49oIElnAl+xfXt9vAbwedsfLRqsASTdbHumM66if5K+Yfuw/tqib+mLo5Ok0HRExPD6FrAIsILt9W2vS7X7yULAt0sGi9mfpB8BnwUWBU6Q9MXCkSJicFbtGRACsH0H1U6gEcNl2z7athv2FA2Uvjg6TWYKRUQMI0n3Aiu715OvpJHA3bbHlEkW3UDSHcDatidLmhf4i+31S+eKiIGRdAbwPPArqmXIe1It496taLAGkPQC8Pe+TpENH/olaX/gU1QXtFrvxwWAq23vWSRYg6Qvjk6TQtMREcPLvQeE6sbJkjJKH7PaK7YnA9h+QZL6+4aI6Eh7A/sDB9XHVwI/LhenUR4gW88PxenAxcDXgMNb2iemnlDb0hdHR8lMoYiIYSTp98A5tn/Rq31P4AO2syNZzDK9rpCLaVd6c4U8omEkzQMsY/ue0lmaJDWFXh+SVgT+aftlSVtRFUv+he2nS+ZqgvTF0WkyKBQRMYwkLQWcQ7VjR892uBsC8wC72H6kYLyYzUladmbnbT80XFkiYvAk7UxVo24u28tLWgc4PhcW+idpou0FJG1m++rSeZpK0niqHfCWAy4FzgNWsb19wViNkL44Ok0GhSIiCpC0DbA61VWhCbYvKxwpukjPVsK2p0haGVgVuDi730U0g6SbgG2AK+oNC5B0W2YY9E/SeNvrZMbQ0PTcf5K+QNWfnCjplp7HY/QvfXF0itQUiogowPafJY0DlgTmkLRM3f5w2WTRJa4EtpC0MHAZcCPwQWCPoqkiol2TbD+TUiSDcpekB4HFJd3W0p6lOwPzqqTdgA8zrUbTnAXzNFH64ugIGRSKiChA0oHAMcCjwJS62VRr8iNmNdXFLfcFTrT9TUm3lA4VEW27Q9LuwEhJY4DPANcUztQItneT9AaqJU9Zbjd4ewOfBL5i+wFJy1PthhftS18cHSGDQhERZRxEtfb+ydJBoitJ0iZUVyP3rdvymiCiOQ4EjgJeptoN6lLgy0UTNYjt/0h6K7AS1QWZ+2y/VDhWI0gabftZ23dSDUYCUA8MnV4wWhOlL46OMKJ0gIiILvUP4JnSIaJrHQQcAfzO9gRJKwCXF84UEW2QNBI4z/ZRtjesP47OoEZ7JM0h6ZtU/fBYqtkt/5D0TUlZ/tS/K3puSOpdD/H3w5qk+dIXR0fISGRERBn3A1dIupDqSi8Atr9bLlJ0kf+27lJk+35arvhGROeyPVnSC5IWtJ2LCwP3LWABYAXbE6Ga/QJ8u/44qGC2JmgtZLXITM5F/9IXR0fIoFBERBkP1x9z1R8Rw+knkuYCTgNOt/102TgRMUAvAbdL+iPwfE+j7byh7N+OwMpu2YLZ9rOS9gfuJoNC/fEMbvd1HDOXvjg6QgaFIiIKsH0cgKQFqkM/VzhSdBHbm9fb3+4N3CjpeuA0238oHC0i2nNh/RED59YBoZbGyZIyqNG/JSQdQjUrqOc29fHi5WI1T/ri6BTq4zkxIiJmMUlrAL9k2tTrJ4AP255QLlV0m7o2yXuAHwDPUr2oP9L2OSVzRUTfJC0OLF4X+W1tXwN41PbjZZI1h6TfA+fY/kWv9j2BD7Qu54nXknTMzM73XPSK9qUvjtIyKBQRUYCka4CjbF9eH28FfNX2piVzRXeQtBbVlckdgD8Cp9i+WdKbgGttL1s0YET0SdKZwI9tj+vV/i7gI7Z3L5OsOSQtBZwDvAjcRLXkaUNgHmAX248UjBddJH1xdIoMCkVEFCDpVttr99cWMStIuhL4GfB/tl/sdW4v278skywiZkbSBNurz+DcHbbXGO5MTSVpG2B1qlkZE2z33kkr+iDpBzM7n7pW7UtfHJ0ig0IREQVI+h1wM9USMoA9gQ1sv6dYqIiI6GiS/mZ75Rmcu8f2KsOdqYkkjQBuyyDawEn6yMzO2x47XFki4vWRQtMREWXsAxxHNYVdwJVUU4gjZjlJY4CvAasBo3raba9QLFREtONeSdvbvqi1UdJ2wP2FMjWO7SmSbpW0jO2HS+dpkgz6vH7SF0enyKBQREQBtp8CMsU6SjkVOAb4HrA11YCkiiaKiHZ8FrhA0geo6uEAbABsQrXVerTvjcCEesen53saU2i6PXXR88N47YDGNsVCNU/64ugIWT4WETGMJP2v7YMlnU9V3HI6eTEaw0HSTbbXl3S77TXrtr/Y3qJ0toiYOUlzA7sDPUufJgCn236pXKrmkbRlX+29i3hH3yT9AfgN8Hngk8BHgMdtH1Y0WIOkL45OkZlCERHDq6eG0LeLpohu91JdU+NeSZ8GHgGWKJwpItpg+2WqGQYxBLbHSVoWGGP7T5LmBUaWztUgi9o+RdJB9UDaOEkZUBuY9MXREUaUDhAR0U1s90z3X8f2uNYPYJ2C0aK7HAzMS7WEcX1gL6qrvBHRAJJ2lXSvpGckPStpoqRnS+dqEkn7Af8H/LRuWgr4fbFAzfNq/fnfknaQtC7w5pKBGuhg0hdHB8jysYiIAiTdbHu9Xm232F63VKaIiGgGSX8HdrJ9V+ksTSVpPLARcF1P39u6jCdmTtKOwF+ApYETgdHAcbbPKxosIgYsy8ciIoaRpN2oakEsL6n1hdMCwJNlUkU3qbcTPgjo2br6LuAHtn9RLlVEDNCjGRAaspdtvyJVdX0lzUEftf6ib7YvqG8+Q1UkOQYgfXF0kgwKRUQMr2uAfwOLAd9paZ8I3FYkUXQNSR+mmq5+CHAz1S4n6wHfkkRejEY0xo2SfkO13Onlnkbb5xRL1DzjJB0JzCNpW+BTwPmFMzWGpJWBHwNL2l5D0lrAzra/XDhax0tfHJ0my8ciIiK6hKS/Ah+y/WCv9uWAM21vXCJXRAyMpL4KTdv2PsMepqHqAr/7Au+kelN+KXCy8+aoLXVR6UOBn7Ysv7vD9hoz/85IXxydJjOFIiIKkLQx1Rr8twBzUe148rzt0UWDxexudO8XoQC2H5SUx15EQ9jeu3SGprM9RdJY4DqqZWP3ZEBoQOa1fX3P8rvapFJhGiZ9cXSU7D4WEVHGD4HdgHuBeYCPUQ0SRcxKLw7yXER0EEkrS7pM0h318VqSji6dq0kk7QDcB/yAqk/+u6TtyqZqlCckrUhdh0nS+6iWx0f/0hdHR8nysYiIAiTdaHsDSbfZXqtuu8b2pqWzxexL0gvA3/s6Baxge75hjhQRg5ClO0Mn6W5gR9t/r49XBC60vWrZZM0gaQXgJGBT4CngAWAP2w8VDdYA6Yuj02T5WEREGS9ImgsYL+mbVFfX8iIgZrW3lA4QEa+LLN0Zusd6BoRq9wOPlQrTNLbvB94haT6q1ScvAh8EMijUv/TF0VEyKBQRUcZeVC+iPg18FlgaeG/RRDHba72CK2lhYJLtiQUjRcTgZOnOIEnatb45QdJFwFlU9+P7gRuKBWuIuubNAcBSwLnAn+rjzwO3Ar8ul64Z0hdHp8nysYiIAiTtBfy+9UWApB1tX1AwVszmJL0J+DrwbmB+4JH61M+Br9h+tVS2iGhflu4M3gx2buuRHdz6IelcqsfctcDbgYWpNsw4yPb4gtEaI31xdJoMCkVEFCDpaeBBYDfbd9VtN9ter2SumL1J+jNwvO0r6qvlWwBHA0cAS9j+eNGAETEgPUt3Mssghouk222vWd8eCTwBLJPHYPvSF0enyaBQREQBkm4B9gV+CRxr+7eSbukpGBoxK0i61fbaLcc32V6/vn13CqxGNIOkRYFjgM2plj5dRfUm88miwRpE0vLAgcBytJTUsL1zqUxN0PsCVi5oDVz64ug0qSkUEVGGbd8saUvgDElvBUaWDhWzvccl7Qn8maqG1YMAqqrVjiiYKyIG5kzgSqbVotsD+A3wjmKJmuf3wCnA+cCUslEaZW1Jz1LtlAUwT8uxbY8uF60x0hdHR8lMoYiIAiRdaHuH+vYI4BvA52znxUDMMpKWAb4NrAaMBw61/e961sFWts8umS8i2tM6s6Cl7UbbG5TK1DSSrrP91tI5ovukL45Ok0GhiIiIiIgGkfRt4EaqnbMA3gesbvuYcqmaRdLuwBjgD8DLPe22by4WqiHqi1m32V6jdJaIGLoMCkVEDCNJ/2v7YEnnU28l3Cq1DGJWkrSY7SdajvcENgLuAH7mvCiIaARJE4H5mLbsaQTwfH07S3jaIOlrwF7AfUy7H217m3KpmkPSr4EjbD9cOkvTSFoE+DTVrmM/B44ENgHuAr5q+6mC8aILZVAoImIYSVrf9k11LaHXsD1uuDNF92gtCCrpaKodT04HdgT+afuzJfNFRAwXSXcDa9l+pXSWJqp30NoQuJ5pA5K5uNUGSRcBtwOjgbfUt88CtgXWtv3ugvGiC6XQdETEMLJ9U/05gz9Rglpu7wpsYft5SacDWTIR0QCS5qIqLL061YzTO4FfZ3BjwG4FFgIeK5yjqY4rHaDB3mR7+7qw9D9tb1W3/0XS+HKxoltlUCgiYhhJup0+lo31sL3WMMaJ7jOPpHWplpqMtP08gO1XJU0uGy0i+iNpNeA84GrgJqqB3q2AoyS92/aEgvGaZkngbkk3MH1Nocx0aYPtcZKWBcbY/pOkeckuqu0aIWlhYAFgfknL2X6wLjQ9V+Fs0YUyKBQRMbx2LB0gutq/ge/Wt/8r6Y0tO55MKpgrItpzIrC/7T+2Nkp6B/BDYOsiqZopRbmHQNJ+wMeBRYAVgaWAnwBvL5mrIb4G3F3f3gc4uZo0xFvIDKwoIDWFIiIiupykkcDctl8onSUiZkzS3bZXncG5u2y/ZbgzRXeqlzltBFxne9267XbbaxYN1hB1vyvbkyTNAawDPGL732WTRTcaUTpAREQ3kXRV/XmipGdbPiZKerZ0vug+ko61PTkDQhGNMELS3L0bJY0iKwAGpFc//JKkyemHB+Tl1jpW9cBGZhu0qe53J9W3JwE7ZkAoSsmgUETEMLK9ef15AdujWz4WyBbCUUjqZ0Q0xy+AsyUt19NQ3z4L+GWhTI3Uqx8eBbyXagletGecpCOpatVtC/wWOL9wpiZLXxzFZFAoIqIASa958d5XW8QwUP9fEhGdwPaXgUuAKyU9IekJYBzwR9vHl03XbLZ/D2xTOkeDHA48TrWd+ieAi4CjiyZqtvTFUUymmUZElLF660E97Xr9Qlmiu+VxF9EQkg6y/f26nsutALYnlk3VTJJ2bTkcAWxAlj+1zfYUSWOB66jut3ucYrVDkb44ismgUETEMJJ0BNAz3bqndoGAV4CTigWLriHpXcB7qHaKMfAvSefavqRosIhox97A94Ef2F6vdJiG26nl9iTgQeDdZaI0j6QdqHYbu4/qdczykj5h++KyyZohfXF0kuw+FhFRgKSv2T6idI7oLpL+F1iZqi7JP+vmNwMfBu61fVChaBHRBklnAJsAi1O9GZ96CrDttYoEi64j6W6q4sh/r49XBC6c0e54MU364ug0GRSKiBhGknpf2TXwhO1/lMgT3UXS32yv3Ee7gL/ZHlMgVkQMgKQ3AJfSR2Fa2w8Nf6JmkfSlmZy27ROGLUyDSbrS9ttajgWMa22LvqUvjk6T5WMREcPrO320LSJpLmA32+OHOU90l5ckbWT7+l7tGwIvlQgUEQNj+z+S3gqsRHVh4T7b+ftt3/N9tM0H7AssCmRQaCZaajFNkHQR1c53Bt4P3FAsWLOkL46OkplCEREdQNIGwHdzhS1mpXqm2o+BBZg2ZX1p4FngU7ZvKpUtIvpXb0rwVaraQg9TFUh+M3AqcJTtVwvGaxxJCwAHUQ0InQV8x/ZjZVN1NkmnzuS0be8zbGEaKn1xdJoMCkVEdAhJN6dwaAyHevnJUlR1SP5p+z+FI0VEGyR9j+qN5Gd7dh2TNBr4NvBiapG0R9IiwCHAHsBY4Pu2nyqbKrpN+uLoFBkUiojoAJKWBC6ynS1JY5aSNAKmbic8F7AG8KDt/5ZNFhH9kXQvsHLvrb8ljQTuTi2S/kn6FrAr1Y6f/8/2c4UjNZKk5YEDgeVoKUli+zW1rmJ6dd/7as/fsaStgfWACdl9LErIoFBExDCSdCLV2vtWiwCbAgfZPn/4U0W3kPQe4KfAFOCTwJFU9TVWBvbP4y+is82oQG1/52IaSVOAl6m2oW/tj3t2cBtdJFjDSLoVOAW4napPAcD2uGKhGqK+77ay/ZSkQ4FdgIuALYEbszttDLcMCkVEDCNJH+nVZOBJ4IbUMYhZTdItwHbAPMCtwIa275G0LHC27Q2KBoyImZL0e+Ac27/o1b4n8IHM0ojhIuk6228tnaOJJN1he4369o3AFrZfrGuG3Wx7rbIJo9tk97GIiGFke2zpDNHdemoWSHrY9j1120M9y8oioqMdAJwjaR/gJqoLCxtSDfTuUjJYdJ3vSzoG+APVzCsAbN9cLlJjPCtpDdt3AE8Ao4AXqd6bpy+OYZdBoYiIAiTdzmuXkT0D3Ah82faTw58quoGkEbanAPu0tI0E5iqXKiLaYfsR4K2StgFWp1rydLHty8omiy60JrAXsA3Tlo+5Po6Z+yTw63oZ2WPAjZLGAWtR7S4YMayyfCwiogBJ3wQmA6fXTR+qPz8LbG57pyLBYrYmaUPgdtsv9Wpfjupx96siwSKibfWsvtt6lp9ElCDpbmAt26+UztJE9cWYd1LV9JuDamv6S20/XTJXdKcMCkVEFCDpatub9dUm6Xbba5bKFt1B0jzAMj1LyCKiOST9GjjC9sOls0R3kvQb4MDUQxya9MXRCbJmMSKijPklTS3QKGkjYP76cFKZSNEtJO0EjAcuqY/XkXRe0VARMRBvBCZIukzSeT0fpUNFV1kSuFvSpXkMDk764ugUqSkUEVHGx4CfS5qfqibEs8DHJM0HfK1osugGxwIbAVcA2B4vafmSgSJiQI4rHSC63jGlA8wGjuW1ffFyBfNEl8qgUEREAbZvANaUtCDVUt6nW06fVSZVdJFJtp+R1NqW9eQRDWF7nKRlgTG2/yRpXmBk6VzRPWyPK51hNtBXXxwx7DIoFBFRgKS5gfcCywFz9LwgsH18wVjRPe6QtDswUtIY4DPANYUzRUSbJO0HfBxYBFgRWAr4CfD2krmie0iayLSLCXMBcwLP2x5dLlXjpC+OjpCaQhERZZwLvJuqftDzLR8Rw+FAqu2sXwbOoFq+eHDJQBExIAcAm1H97WL7XmCJoomiq9hewPbo+mMU1YWuH5bO1TDpi6MjZPexiIgCJN2R7YQjImIwJF1n+62SbrG9rqQ5gJttr1U6W3QvSX+1vXHpHBExMFk+FhFRxjWS1rR9e+kg0X0kbQAcSb18sac9bygjGmOcpCOBeSRtC3wKOL9wpugiknZtORwBbEBq0w1I+uLoFJkpFBFRgKQ7gZWAB6imDQtwXgjEcJB0D3AocDswpafd9kPFQkVE2ySNAPYF3knVf1wKnOy8sI9hIunUlsNJwIPAz2w/ViZR86Qvjk6RQaGIiALqXWNeIy8EYjhIusr25qVzRMTgSZoLWJVqdsY9tl8pHCkiBiB9cXSKDApFRAwjSaNtPytpkb7O2/7vcGeK7iPp7cBuwGVUM9UAsH1OsVAR0TZJO1DtNnYf1Uyh5YFP2L64aLCY7Un60kxO2/YJwxam4dIXR6fIoFBExDCSdIHtHSU9QHV1Vy2nbXuFQtGii0j6FdUMgwlMm7Ju2/uUSxUR7ZJ0N7Cj7b/XxysCF9petWyymN1J+lwfzfNRLWdc1Pb8wxypsdIXR6fIoFBERESXkXS77TVL54iIwZF0pe23tRwLGNfaFjGrSVoAOIhqQOgs4DupKdS+9MXRKbL7WEREAZLOBc4EzrX9Quk80XX+Kmk123eWDhIR7WvZ8WmCpIuo3ogbeD9wQ7Fg0VXqJfCHAHsAY4H1bD9VNlUjpS+OjpCZQhERBUjaEvggsANwPfAb4ALbLxUNFl1B0l3AimT3u4hG6bXjU29ZdhKznKRvAbsCJwH/z/ZzhSM1Vvri6BQZFIqIKEjSSGAbYD/gf2yPLhwpukB2v4uIiMGQNIVqAGMS1Sy1qaeoBjTyOqZN6YujU2T5WEREIZLmAXaimjG0HtUU7IhZpmf3O2Bi6SwRMXiSlgcOBJaj5fW87Z1LZYruYHtE6QxNl744Ok1mCkVEFCDpN8BbgUuoakJcYXvKzL8rYmiy+13E7EHSrcApwO1M27UI2+OKhYqItqQvjk6TmUIREWWcCuxue3LpINFVTgWwvXzpIBExJC/Z/kHpEBExKOmLo6NkplBExDBq2TmmT7bPGa4s0X0k3Wx7vdI5ImJoJO0OjAH+QFXfBQDbNxcLFRFtSV8cnSYzhSIihtdOMzlnIINCERHRnzWBvag2KuhZPub6OCIiom2ZKRQREdElJL0A/L2vU2Qb3IjGkHQ3sJbtV0pniYiBSV8cnSYzhSIiCpC0IHAM8La6aRxwvO1nyqWKLvAAM5+tFhHNcCuwEPBY4RwRMXDpi6OjZFAoIqKMnwN3AB+oj/eiKjw405pDEUP0su2HSoeIiCFbErhb0g1MX1MoW9JHdL70xdFRsnwsIqIASeNtr9NfW8TrSdJE2wtI2sz21aXzRMTgSNqyr/ZsSR/R+dIXR6fJTKGIiDJelLS57asAJG0GvFg4U8z+7qs/nwhk55OIhsrgT0SjpS+OjpJBoYiIMvYHxta1hQCeAj5SME90h7skPQgsLum2lvYUt4xoEEkTqXYbA5gLmBN43vbocqkiok3pi6OjZPlYREQBkkbanixpNIDtZ0tniu4g6Q3ApcBrao+kxkFEM0l6D7CR7SNLZ4mI/qUvjk6SQaGIiAIkPQxcAvwG+LPzZBzDSNIoYCWqmQb32X6pcKSIGCJJf7W9cekcEdGe9MXRKbJ8LCKijFWotiM9ADhF0gXAmT01hiJmBUlzAF8F9gYeBkYAb5Z0KnCU7VdL5ouI9khq3alyBLAB05aTRUQHS18cnSYzhSIiCpO0MPB9YA/bI0vnidmXpO8BCwCftT2xbhsNfBt40fZBJfNFRHvqN489JgEPAj+z/ViZRBHRrvTF0WkyKBQRUUi9pfAHge2AG4Df2D67bKqYnUm6F1i593JFSSOBu22PKZMsIiKiO6Qvjk6T5WMREQVIegAYD5wFHGr7+bKJoku4r/pVddHzXCWK6HCSvjST07Z9wrCFiYjBSl8cHWVE6QAREV1qbdu72D4jA0IxjO6U9OHejZL2BO4ukCciBub5Pj4A9gUOKxUqIgYkfXF0lCwfi4goQNLKwI+BJW2vIWktYGfbXy4cLWZjkpYCzgFeBG6iKky7ITAPsIvtRwrGi4gBkLQAcBDVgNBZwHdSUyii86Uvjk6TQaGIiAIkjQMOBX5qe9267Q7ba5RNFt1A0jbA6oCACbYvKxwpItokaRHgEGAPYCzwfdtPlU0VEQOVvjg6RWoKRUSUMa/t6yW1tk0qFSa6h6QRwA8yABnRPJK+BewKnASsafu5wpEiYhDSF0cnSU2hiIgynpC0ItWUYSS9D/h32UjRDWxPAW6VtEzpLBExYJ8D3gQcDfxL0rP1x0RJzxbOFhFtSl8cnSQzhSIiyjiA6krvqpIeAR6gWgoQMRzeCEyQdD3TCtVie+dykSKiP7ZzQTdi9pG+ODpCagpFRBQkaT6qWZsvAh+0/evCkaILSNqyr3bb44Y7S0RERDdKXxydIoNCERHDSNJoqllCSwHnAn+qjz8P3Gr73QXjRReRtCwwxvafJM0LjLQ9sXSuiIiIbpG+ODpBBoUiIoaRpHOBp4BrgbcDCwNzAQfZHl8wWnQRSfsBHwcWsb2ipDHAT2y/vXC0iIiIrpC+ODpFBoUiIoaRpNttr1nfHgk8ASyTq0IxnCSNBzYCrrO9bt029bEZERERs1b64ugUKVYXETG8Xu25YXsy8EAGhKKAl22/0nMgaQ7qnfAiIiJiWKQvjo6Q3cciIobX2i3bBguYpz4WYNujy0WLLjJO0pFUj79tgU8B5xfOFBER0U3SF0dHyPKxiIiILiNpBLAv8E6qAclLgZOdFwURERHDIn1xdIoMCkVERHQhSXMBq1JNVb+ndQp7REREzHrpi6MTZFAoIiKiy0jaAfgJcB/V1cnlgU/YvrhosIiIiC6Rvjg6RQaFIiIiuoyku4Edbf+9Pl4RuND2qmWTRUREdIf0xdEpsvtYRERE93ms50Vo7X7gsVJhIiIiulD64ugI2X0sIiKiS0jatb45QdJFwFlUdQzeD9xQLFhERESXSF8cnSaDQhEREd1jp5bbjwJb1rcfBxYe/jgRERFdJ31xdJTUFIqIiIiIiIiI6EKZKRQREdFlJC0PHAgsR8trAds7l8oUERHRTdIXR6fIoFBERET3+T1wCnA+MKVslIiIiK70e9IXRwfI8rGIiIguI+k6228tnSMiIqJbpS+OTpFBoYiIiC4jaXdgDPAH4OWedts3FwsVERHRRdIXR6fI8rGIiIjusyawF7AN06asuz6OiIiIWS99cXSEzBSKiIjoMpLuBtay/UrpLBEREd0ofXF0ihGlA0RERMSwuxVYqHSIiIiILpa+ODpClo9FRER0nyWBuyXdwPR1DLINbkRExPBIXxwdIYNCERER3eeY0gEiIiK6XPri6AipKRQRERERERER0YUyUygiIqLLSJpItcMJwFzAnMDztkeXSxUREdE90hdHp8igUERERJexvUDrsaT3ABuVSRMREdF90hdHp8jysYiIiEDSX21vXDpHREREt0pfHCVkplBERESXkbRry+EIYAOmTWGPiIiIWSx9cXSKDApFRER0n51abk8CHgTeXSZKREREV0pfHB0hy8ciIiIiIiIiIrpQZgpFRER0CUlfmslp2z5h2MJERER0ofTF0WkyUygiIqJLSPpcH83zAfsCi9qef5gjRUREdJX0xdFpMigUERHRhSQtABxE9SL0LOA7th8rmyoiIqJ7pC+OTpDlYxEREV1E0iLAIcAewFhgPdtPlU0VERHRPdIXRyfJoFBERESXkPQtYFfgJGBN288VjhQREdFV0hdHp8nysYiIiC4haQrwMtXWt60vAERV3HJ0kWARERFdIn1xdJoMCkVEREREREREdKERpQNERERERERERMTwy6BQREREREREREQXyqBQREREREREREQXyqBQREREREREREQX+v8rdgOC5IBnOwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "#取除了Unnamed: 0之外的所有标签\n",
    "features = train.dtypes[train.dtypes != \"object\"].index\n",
    "features = features.drop('Unnamed: 0')\n",
    "#绘制热力图查看特征与特征之间的相关性\n",
    "corr = train[features].corr()\n",
    "plt.figure(figsize = (18, 15))\n",
    "pic_sns = sns.heatmap(corr, annot=True, fmt='.2g', cmap = 'GnBu')\n",
    "# bottom, top = pic_sns.get_ylim()\n",
    "# pic_sns.set_ylim(bottom + 0.5, top - 0.5)\n",
    "pic_sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "观察发现，三个逾期还款的特征之间相关度都很高，且接近1，有可能出现了极端情况。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
